Research ArticleEvaluating the Risk of Natural Gas Pipeline OperationManagement in Intuitionistic Fuzzy Linguistic Environments

Ming Li 1 Liangliang Liu 12 Ying Li1 and Yingcheng Xu3

1School of Business Administration China University of Petroleum-Beijing Beijing 102249 China2Kunlun Insurance Brokers Co Ltd Beijing 100033 China3China National Institute of Standardization Beijing 100191 China

Correspondence should be addressed to Liangliang Liu makeinnovationsfoxmailcom

Received 2 March 2018 Revised 9 July 2018 Accepted 29 July 2018 Published 13 August 2018

Academic Editor Emilio Jimenez Macıas

Copyright copy 2018 Ming Li et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Natural gas pipeline has been regarded as the most cost effective and safest channel of gas transportation and has extraordinarystrategic significance for the countryThere is a need to control the risks during the operation so as to operate the pipeline safely andsmoothly under the complicated business environment In order to evaluate the risk of natural gas pipeline operationmanagementthis paper proposed an approach to evaluate the risk of natural gas pipeline operationmanagement in intuitionistic fuzzy linguisticenvironments Firstly the evaluation criteria of the risk of natural gas pipeline operation management are established from thestrategic risk market risk financial risk operation risk and legal risk aspects Then experts are invited to evaluate the risk leveland the weight of criteria using the linguistic terms Rating values with regard to the criteria are in linguistic forms the linguisticterms are modeled with the intuitionistic fuzzy linguistic model The linguistic ratings are aggregated as the final results Finallythe example is given to illustrate the feasibility and practicability of the proposed method

1 Introduction

Oil and gas are strategic to a country and play a crucialrole in economic development [1] Pipeline is regarded asthe most cost effective and safest channel to transport theoil and gas from upstream oil field or port to the down-stream users or refineries [2] It will bring large scale ofloss and chaos to the society if the pipeline is accidentalmalfunction It is necessary for the pipeline operators toidentify eliminate control avoid or transfer the risk incase of accident or enterprise operation break in the normaloperation Risk of pipeline operation becomes a hot topicamong the international pipeline operation companies onhow to ensure that the pipeline transportation is safe andeffective [3ndash11] Many studies of the risk of pipeline operationare conducted from various aspects For example the riskof operating cross-country petroleum pipelines in India isanalyzed [3] The project risks are analyzed and applied toan oil pipeline construction project in India [4] The caseof Savadkooh in Iran is studied for Pipeline risk assessment[5] The emerging threats to natural gas pipeline systems

after a natural disaster are detected [6] The risk to the longgas and oil pipeline project in China caused by landslidesduring its construction is assessed [7] Reliability and riskof a port oil pipeline transportation system in variableoperation conditions are evaluated [8] Urban natural gaspipeline networks are assessed from the qualitative aspectsand the quantitative aspect [9] The uncertainty involved inthe pipeline risk assessment modeled with the fuzzy logic isdeveloped [10] The quantitative risk assessment for naturalgas pipelines is proposed [11] However these researches putfocus on the technical perspective but not the operationmanagement perspective

In order to resolve the problem in this paper we pro-posed the approach to the evaluation of the risk of natural gaspipeline operation management Firstly the evaluation cri-teria are established The risks are identified from enterprisestrategymarketing operation finance and law aspects Sincethese risks are difficult to be measured quantitatively thelinguistic values of expert evaluation are preferred Secondlywith intuitionistic fuzzy set [12ndash14] the method for dealingwith the linguistic evaluation information is proposed

HindawiMathematical Problems in EngineeringVolume 2018 Article ID 3960496 13 pageshttpsdoiorg10115520183960496

2 Mathematical Problems in Engineering

The structure of the rest of this paper is as followsSection 2 reviews the intuitionistic fuzzy set In the followingsection the evaluation criteria are constructed Section 4presents the method to deal with linguistic informationSection 5 provides an illustrate example The final sectionconcludes the paper

2 Intuitionistic Fuzzy Sets

Due to the complexity and uncertainty of candidates somecriteria are more suitable for evaluation in linguistic formThe linguistic terms are mostly modeled with triangularfuzzy number [15 16] Afterwards intuitionistic fuzzy sets areproposed to model the linguistic terms [17] Compared withfuzzy sets which only consider the degree of membershipintuitionistic fuzzy sets is characterized by both a member-ship function and a nonmembership function and the sum ofboth values is allowed to be less than one [18] It has been usedsuch as in the area of search algorithm selection [19] RuralLogistics Center Location [20] information system projectselection [21] and green supply chain [22] In the followingintuitionistic fuzzy sets are reviewed briefly [17 18 23ndash25]

Definition 1 (see [17]) Let X be a given finite set then definean intuitionistic fuzzy set on X as A

119860 = [119909 120583119860 (119909) V119860 (119909)] | 119909 isin 119883 (1)

in which 120583119860 119883 997888rarr [0 1] and V119860 119883 997888rarr[0 1] are respectively the membership function and thenonmembership function with the condition 0 le 120583119860(119909) +V119860(119909) le 1

In addition 120587119860(119909) = 1 minus 120583119860(119909) minus V119860(119909) represents theintuitionistic fuzzy index or hesitancy degree of whether 119909belongs to 119860 or not with the condition 0 le 120587119860(119909) le 1Definition 2 (see [18]) Arithmetic operations on intuitionis-tic fuzzy numbers

Let both 120572 = (120583119860 V119860) and 120573 = (120583119861 V119861) be intuitionisticfuzzy numbers and 120582 be real number The arithmetic opera-tions on intuitionistic fuzzy numbers are defined as follows

(1) 120572 oplus 120573 = (120583119860 + 120583119861 minus 120583119860120583119861 V119860V119861) (2)

(2) 120582120572 = (1 minus (1 minus 120583119860)120582 V119860120582) 120582 gt 0 (3)

(3) 120572 otimes 120573 = (120583119860120583119861 V119860 + V119861 minus V119860V119861) (4)

(4) 120572120582 = (120583120582119860 1 minus (1 minus V119860)120582) 120582 gt 0 (5)

(5) 120572120573 = (min (120583119860 120583119861) max (V119860 V119861)) (6)

Definition 3 (see [25]) For intuitionistic fuzzy numbers thescore function of 120572 is defined as

119904 (120572) = 119896120583120572 minus 119896V120572 + (1 minus 2119896) 10038161003816100381610038161 minus (120583 + V120572)1003816100381610038161003816 (7)

where k reflects the decision attitude of the evaluator and 0 lt119896 lt 1

Definition 4 (see [24]) Entropy for intuitionistic fuzzy num-bers is as follows

For intuitionistic fuzzy set 119860 isin 119868119865119878(119909) the improvedentropy is defined as

119864 (A) = 1n

119899sum119894=1

[120587119860 (119909119894) + radic22 (1 minus 120587119860 (119909119894))

minus radic22 1003816100381610038161003816120583119860 (119909119894) minus V119860 (119909119894)1003816100381610038161003816](8)

Definition 5 (see [23]) Cross entropy of intuitionistic fuzzynumbers is as follows

For intuitionistic fuzzy set 119860 119861 isin 119868119865119878(119909) the crossentropy is defined as

119863 (119860 119861) = 119899sum119894=1

[120583119860 (119909119894) ln 120583119860 (119909119894)(12) (120583119860 (119909119894) + 120583119861 (119909119894))+ V119860 (119909119894) ln V119860 (119909119894)(12) (V119860 (119909119894) + V119861 (119909119894))]+ 119899sum119894=1

[120583119861 (119909119894) ln 120583119861 (119909119894)(12) (120583119860 (119909119894) + 120583119861 (119909119894))+ V119861 (119909119894) ln V119861 (119909119894)(12) (V119860 (119909119894) + V119861 (119909119894))]

(9)

3 Evaluation Criteria of the Risk of NaturalGas Pipeline Operation Management

Natural gas is a national strategic resource and its prod-uct characteristic determines its unique risk in the marketcompetition From the perspective of overall corporate riskpipeline operation company like other enterprises shares thesame risks in terms of strategy law and human resourcesAccording to [26 28 29] the first level is gotten Then theevaluation criteria are established based on [11 26 28ndash70]which are shown in Table 1

31 Strategic Risk Strategic risk of the natural gas pipelinecompany includes the external macro environment theindustries the resources and capabilities in the enterprises[30ndash32]

311 Macro Environment National policies and regulationshave influences on themacro environment To encourage andsupport the exploration and utilization of natural gas a seriesof preferential policies are issued [33] If these preferentialpolicies change the company will be influenced significantlyPipeline companies sell natural gas to downstream industrialand commercial enterprises to make profits [34] If thedomestic economy goes down the pipeline enterprises willface the decrease of downstream users and the reduction ofgas consumption [35]

Mathematical Problems in Engineering 3

Table 1 Evaluation criteria of the risk of natural gas pipelineoperation management

First level Second level

Strategicrisk(C1)[30ndash32]

Macro environment(C11) [33ndash35]Industry environment(C12) [36]

Internal resources andcapabilities(C13) [37]

Marketrisk(C2)[38ndash40]

Downstream user(C21) [41]Imbalance between supply and

demand(C22)Price(C23)

Competition(C24)

Financialrisk(C3)[42 43]

Debt paying ability(C31)Operating ability(C32) [43ndash51]Profitability ability(C33) [52]Development ability(C34) [53]

Operationrisk(C4)[26 54 55]

Human resources(C41) [56ndash59]Material purchase (C42) [60ndash62]

Management system(C43)HSE(C44) [63ndash65]

Legalrisk(C5) [63]

Contract(C51) [66 67]Safety and environmental

protection law(C52) [11 68ndash70]Labor Law(C53)

Operating compliance(C54)

312 Industry and Competition Risk In most countries thenatural gas industry is monopolized [36] The transportationand sales of natural gas are controlled by the natural gaspipeline company The alternative pipeline transportationproduct such as LNG photovoltaic and wind energy willbreak the monopoly Meanwhile with the introducing ofnew competitors companies will have to reduce margins toparticipate in competitions

313 Internal Resources and Capacity Risk Strategic plan-ning implementation investment decision-making andoperational capability have influence on the internalresources and capacity risk [37] It is mainly reflected in theunreasonable strategic decision-making market positioningis not accurate and the development strategy is not clearThe mismatch between the organizational structure andoperation mode and the development strategy will lead tothe results that the implementation of the strategy is blockedand the strategic awareness of all staff in the organization isweak and the planning and execution of the unit cannot becarried out in good accordance with the corporate strategy

32 Market Risk Market risk refers to the interactionbetween participants in the market competition whichcauses the change of supply and demand in the market andthe uncertainty of this change forms the market risk [38]

The market risk factors of the enterprise can be summedup in four aspects downstream user risk unbalanced market

supply and demand risk market price risk and competitionrisk [39 40]

321 Downstream User Risk Downstream natural gas pipe-line users include industrial users city gas LNG and CNG[41] Downstream user risk is mainly reflected in the gasconsumption instability and downstream users stickinessinstability The instability of the gas season will bring unbal-anced supply andmarketing risks to the upstream explorationand development and midstream pipeline transportationenterprises In the precondition that the gas has no muchmore price advantage compared with the oil coal and othertraditional energy fuel the cost of equipment replacementand branch line construction will reduce the customer stick-iness

322 The Imbalance between Supply and Demand RisksThe relationship between supply and demand in the marketdetermines the position of buyers and sellers in the marketFor example in this country with undeveloped industry theaverage daily gas consumption is much less than the optimaldesigned annual gas transmission volume As a result thenew developed natural gas field is overproduction forming abuyerrsquos market and the profit margins of upstream gas fieldsandmidstream processing plants and pipeline enterprises aredeclining

323 Price Risk The major income resource of the naturalgas pipeline companies is pipeline transportation fee Thepricing mechanism of natural gas prices mainly refer to theprice of natural gas and natural gas substitute fuels suchas oil prices in the international market and then calculatethe domestic various types of rates tax rates and upstreammining costs and profits to make a comprehensive pricingWith the increase of postproduction costs and operatingcosts natural gas prices will be raised accordingly Gas powerplants ceramics factories and cement plants are sensitive toprice changes Natural gas prices rise will inevitably lead tothe loss of some customers

324 Competition Risk The market competition risks ofnatural gas pipeline enterprises are mainly reflected in thecompetition risks of the main alternative energy utilizationand competitor risks The technological development andprice changes of alternative energy sources will directlycompete with the consumption of natural gas With thedevelopment of technologies such as LNG and LPG moreand more countries export LNG and LPG and LNG andLPG in the international market will also compete withtheir own countriesrsquo natural gas enterprises Meanwhile theother natural gas pipeline will also compete with pipelinecompanies for customer resources

33 Financial Risk Corporate financial risk refers to the riskof financial loss caused by the lack of financial rules andregulations financial operations and fund management andother noncompliance The financial risk includes corporatesolvency capital operation ability corporate profitability andsustainable development capacity aspects [42 43]

4 Mathematical Problems in Engineering

331 Corporate Solvency Risk The liquidity ratio and thequick ratio reflect the ability of the enterprise to repay thecurrent debt in the short term and they are respectivelyequal to the circulating assets and the ratio of the quickassets to the cash liabilities in a financial period Theratio of firmrsquos equity reflects the strength and weakness ofthe stability of the basic financial structure from a singleperspective Interest guarantee rate is used to measure theprofitability of the enterprise in the current period to repayinterest The cash flow debt ratio of a company is used tomeasure the current ability of an enterprise to repay short-term debts Long-term asset suitability of enterprises canbe used to measure the strength of long-term corporatesolvency

332 Enterprise Operational Capacity Risk The analysis ofthe risk of an enterprisersquos operational capability can be startedby calculating the financial indicators related to the turnoverof the enterprisersquos capital [43ndash45] Enterprise accounts receiv-able [46] turnover rate can be used to measure the flowof corporate accounts receivable The enterprise inventoryturnover rate [47] can be used tomeasure the turnover rate ofthe enterprise inventory Liquidity [48] turnover can be usedto measure the profitability of liquid assets in the enterpriseThe total asset turnover [49] of an enterprise can be used tomeasure the rate at which an enterprise invests its assets intooutput during the business cycle Nonperforming assets ratio[50] refers to the ratio of nonperforming assets to the totalassets of an enterprise in a financial period which can be usedtomeasure the nonperforming assets of enterprisesThe assetloss [51] rate refers to the ratio of the loss on fixed assets to thebeginning of the total fixed assets in a financial period whichreflects the loss of fixed assets of the enterprise

333 Enterprise Profitability Risk The analysis of corporateprofitability risk can be measured from the following indica-tors [52] Operating profit margins measure howmuch profita business unit makes Return on total assets can measure thesize of the companyrsquos assets operating efficiency Capital rateof return can reflect the ability of its own capital to obtain netincomeThe cost-of-business profit marginmeasures the costof investing the profit per unit of business

334 Enterprise Development Capability Risk Analysis ofenterprise development capability risk can start with thefollowing indicators Sales growth rate refers to the ratioof sales revenue growth and total sales revenue in the lastfinancial cycle in a financial cycle It is an important indicatorto reflect whether the enterprise is in good condition and themarket possession ability is strong or weak [53]

34 Operational Risk Operational risks of natural gas pipe-line operation management are classified into human re-source risk material procurement management risk man-agement system risk and HSE risk [26 54 55]

341 Human Resource Risk Human resources risk is mainlyin the following areas [56ndash58] The unqualified person may

be recruited into the enterprise due to the asymmetricinformation in the labor market or the unclear job demandanalysis of the recruiters The loss of staff wastage occurswhen the employeersquos contribution to the enterprise has notreached the capital cost invested by the enterprise Employeesneglect their duties abuse their powers or violate the enter-prise management system to cause financial and reputationlosses to the enterprise Staff injuries cause the enterpriseincome loss and expenses Human resource managersrsquo lack-ing of ability and management ethics causes the managerialcorruption [59]

342 Material Procurement Risk Thematerial procurementis mainly reflected in three aspects procurement cost pro-curement quality and procurement time [60] When it isimpossible to purchase materials and spare parts for thedaily operation and maintenance of the pipeline at home theenterprises must invite tenders fromabroad for procurementThe procurement cost risk is incurred in international pro-curement due to price fluctuation exchange rate fluctuationand default of bidders Purchasing quality is influencedby suppliersrsquo noncompliance of supplier quality incompletequality supervision and unqualified quality inspection Theprocurement time also has influence on the risk since the pro-duction operation is blocked due to the poor procurementthe equipment operation risk increases and the emergencyrequirement cannot be satisfied [61 62]

343 Management System Risk The risk of managementsystem ismainly manifested in the fact that due to this naturalgas pipeline management company just taking over the oper-ation from the construction parties various managementsystems in daily operation and management have not beenestablished yet It will lead to risks such as unscrupulousoperation inefficiency and poor risk control

344 HSE Risk HSE (Health Safety and Environment)[63] risk refers to the risk caused by the HSE managementwhich is not in place and HSE management system is notestablished or not completed HSE budget is insufficientSafety hazards may not be rectified timely Many other factorsalso have influence on the HSE risk such as lacking of staffsafety education fatal accidents pipeline explosion leakageaccidents and employee occupational injuries [64 65]

35 Legal Risk Natural gas transmission pipeline has longdistance and wide coverage The relationship between enter-prises and local governments is complicated There are con-tract risks legal risks of safety and environmental protectionlabor law risks and operation compliance risks in operationof natural gas pipeline [63]

351 Contract Risk The contract risk is reflected in thesigning and fulfillment of the contract [66] The naturalgas pipeline company is in the middle position in the oiland gas industry chain and plays a role of transformationTherefore many contracts and market transactions existbetween pipeline companies and upstream gas fields anddownstream users Once the contract is formulated and

Mathematical Problems in Engineering 5

implemented the enterprise will face the risk of breach ofcontract [67]

352 Safety and Environmental Legal Risks Natural gas pipe-line transportation industry belongs to the high-risk industry[11 68] The massive natural gas leakage will cause somepollution to the environment [69 70] The high-pressurenatural gas pipeline passes through the villages and citiesalong the pipeline route Once the leakage explosion andfire accidents occur the consequences will be disastrousTherefore the pipeline safety management is under strictsupervision of local authorities

353 Labor Law Risks The operation of pipelines employsmany workers and has diversified employment patternsThere are protection mechanisms for employeesrsquo rightsEspecially with the improvement of the education level of thestaff the awareness of rights protection is very strong

354 Operation Compliance Risk Operation compliancerisks are mainly reflected in the business-related licenses orqualifications that have not been obtained for a businessThere is no synchronization with the latest laws regulationsor industry standards promulgated by the state Companymay be charged with illegal business operations and so on

4 Method to Deal with the LinguisticEvaluation Information in IntuitionisticFuzzy Linguistic Environment

Let 119864 = 1198901 1198902 119890119904 be the set of evaluators 119898119894 be thealternative and 119862 = 1198621 cup 1198622 cup sdot sdot sdot cup 119862119897 = 1198881 1198882 119888119899 bethe criteria The weight vector of the evaluator obtained bysubjective weighting method is 120582 = (1205821 1205822 120582119904)119879 with thecondition 0 le 120582119896 le 1 119896 = 1 2 119904 and sum119904119896=1 120582119896 = 1

Evaluators 119890119896 evaluate the alternative 119898119894 according to thecriterion 119888119895 to obtain the intuitionistic fuzzy decision matrix119877119896 = (120572119896119894119895)119899times119898 under the attribute of the alternative in which120572119896119894119895 = (120583119896119894119895 V119896119894119895) 0 le 120583119896119894119895 le 1 0 le V119896119894119895 le 1 0 le 120583119896119894119895+V119896119894119895 le1 120587119896119894119895 = 1 minus 120583119896119894119895 minus V119896119894119895 and 120583119896119894119895 V119896119894119895 120587119896119894119895 respectively expresses thesatisfaction dissatisfaction and hesitancy degree of evaluator119890119896 under the attributes 119888119895 of the alternative 119898119894

The higher the consistency of expertsrsquo opinion on eval-uation of criteria weight the greater the weight of expertsin weight evaluation [71] More experts agree on the criteriaweight and risk assessment the more understanding of thecriteria and the enterprise risk the greater the weight of theexperts in the risk assessment In the following based on thework [23ndash25 71 72] the linguistic evaluation information isdealt with by the following steps

Step 1 Assume that the rating of alternative mi with respectto criterion 119888119895 given by the evaluators 119890119896 can be expressedin IFS 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) the rating of importance ofcriterion 119888119895 given by the evaluators119890119896 can be expressed inIFS 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 ) Therefore the corresponding

intuitionistic fuzzy decision matrices 119877(119896) and 119882(119896) can beexpressed in matrix form concisely as follows

119877(119896) = (119903(119896)119894119895 )119899times119898 =[[[[[[[[

119903(119896)11 119903(119896)12 sdot sdot sdot 119903(119896)1119899119903(119896)21 119903(119896)22 sdot sdot sdot 119903(119896)2119899 119903(119896)1198981 119903(119896)1198982 sdot sdot sdot 119903(119896)119898119899

]]]]]]]]

119882(119896) = (119908(119896)1 119908(119896)2 119908(119896)119899 )

(10)

where 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 )Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21According to (3) construct the aggregated intuitionistic

fuzzy decision matrix 119875 = (1205751 1205752 120575119899) based on theimportance of criteria by

120575119895 = 119904⨁119896=1

119908(119896)119895 120582119896 = (120583119895 V119895) (11)

120583119894 = 1 minus 119904prod119896=1

(1 minus 120583(119896)119895 )119908(119896)119895

V119896119894 =119904prod119896=1

(V(119896)119895 )119908(119896)119895(12)

Step 22According to (3) construct the initial aggregated intu-

itionistic fuzzy decision matrix 119876 = (120572119894119895)119899times119898 based on therating of alternative by

120572119894119895 = 119904⨁119870=1

119903119896119894119895120582119896 = (120583119894119895 V119894119895)

= (1 minus 119904prod119896=1

(1 minus 120583119896119894119895)120582119896 119904prod119896=1

(V119896119894119895)120582119896)(13)

Step 3 Obtain the entropy weightStep 31Based on (8) obtain the entropy weight119867119888(119890119896) of evalua-

tors 119890119896 on the evaluation of criteria by

119864119896119888 = 1n

119899sum119894=1

[120587(119896)119895 + radic22 (1 minus 120587(119896)119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119895 minus V(119896)11989510038161003816100381610038161003816] (14)

119867119888 (119890119896) = (1 minus 119864119896119888)(119904 minus sum119904119896=1 119864119896119888 ) (15)

6 Mathematical Problems in Engineering

Step 32Based on (8) obtain the entropy weight 119867119898119894(119890119896) of

evaluators on the evaluation of alternative 119898119894 by119864119896119894 = 1

n

119899sum119894=1

[120587(119896)119894119895 + radic22 (1 minus 120587(119896)119894119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119894119895 minus V11989611989411989510038161003816100381610038161003816] (16)

119867119898119894 (119890119896) = (1 minus 119864119896119894 )(119904 minus sum119904119896=1 119864119896119894 ) (17)

Step 4 Calculate the cross entropy weightStep 41Based on (9) Calculate the cross entropy weight119862119888(119890119896) of

evaluator 119890119896 on the evaluation of criteria using

119863119888 (119884119896119888 119883119888) = 119899sum119895=1

[[120583(119896)119895 ln

120583(119896)119895(12) (120583(119896)119895 +120583119895)

+ V(119896)119894119895 lnV(119896)119895

(12) (V(119896)119895 +V119895)]]+ 119899sum119895=1

[[120583119895 ln 120583119895

(12) (120583(119896)119895 +120583119895)+ V119895 ln

V119895(12) (V(119896)119895 +V119895)]]

(18)

119862119888 (119890119896) = 1119863119888 (119884119896119888 119883119888)sum119904119896=1 (1119863119888 (119884119896119888 119883119888)) (19)

Step 42Based on (9) calculate the cross entropy weight119862119898119894(119890119896) on the evaluation of alternative 119898119894 using119863119898119894

(119884119896119898119894 119883119898119894) =119899sum119895=1

[[120583(119896)119894119895 ln

120583(119896)119894119895(12) (120583(119896)119894119895 +120583119894119895)

+ V(119896)119894119895 lnV(119896)119894119895

(12) (V(119896)119894119895 +V119894119895)]]+ 119899sum119895=1

[[120583119894119895 ln 120583119894119895

(12) (120583(119896)119894119895 +120583119894119895)+ V119894119895 ln

V119894119895(12) (V(119896)119894119895 +V119894119895)]]

(20)

119862119898119894 (119890119896) = 1119863119898119894 (119884119896119898119894 119883119898119894)sum119904119896=1 (1119863119898119894 (119884119896119898119894 119883119898119894)) (21)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights of evaluator 119890119896

Step 51According to the cross entropy weights and entropy

weights of evaluator 119890119896 on the evaluation of importance ofcriteria we can get the corresponding final weight 120578119896119888 by

120578119896119888 = 120572119862119888 (119890119896) + (1 minus 120572)119867119888 (119890119896) (22)

where 0 le 120572 le 1Step 52According to the cross entropy weights and entropy

weights of evaluators on the evaluation of alternative 119898119894 wecan get the corresponding final weight 120578119870119894 by

120578119896119894 = 120572119862119898119894 (119890119896) + (1 minus 120572)119867119898119894 (119890119896) (23)

where 0 le 120572 le 1Step 53Based on 120578119896119888 and 120578119896119894 calculate the corresponding final

weight 120578119896 which comprehensively considers the consistencyof indicator evaluation and the indicator scores of evaluatorsby

120578119896 = 120579120578119896119888 + (1 minus 120579) 120578119896119894 (24)

where 0 le 120579 le 1Step 6 Calculate the integrated evaluation result matrix

Step 61Integrating the weight based on the rating of criteria

of evaluators based on (4) the integrated evaluation resultmatrix 119861119888 = (1205731 1205732 120573119899) of criteria can be obtained by

120573119895 = 119904sum119896=1

119908(119896)119895 120578119896119888 = (120588119895 120590119895) (25)

Step 62Integrating the weight based on both the rating of

alternatives and the rating of criteria of evaluators based on(4) the integrated evaluation result matrix 119861119898119894 = (120573119894119895)119899times119898 ofalternative 119898119894 is derived by

120573119894119895 = 119904sum119896=1

120572(119896)119894119895 120578119896 = (120588119894119895 120590119894119895) (26)

Step 7 Determine the final evaluation results Based on(2)ndash(6) the weighted evaluation value of the alternative 119898119894with respect to the parent criteria 119862119897 can be derived by

120585119894119897 = sum119888119895isin119862119897 120573119894119895 otimes 120573119895sum119888119895isin119862119897 120573119895 = sum119888119895isin119862119897 (120588119894119895 120590119894119895) otimes (120588119895 120590119895)sum119888119895isin119862119897 (120588119895 120590119895)= (120588119894119897 120590119894119897)

(27)

Step 8 In the condition that the weighted rating of thealternative with the parent criteria needs to be comparedbased on (7) the score function can be used as

119904 (120585119894119897) = 119896120588119894119897 minus 119896120590119894119897 + (1 minus 2119896) 10038161003816100381610038161 minus (120588119894119897 + 120590119894119897)1003816100381610038161003816 (28)

Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

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Mathematical Problems in Engineering

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Mathematical Problems in Engineering 3

Table 1 Evaluation criteria of the risk of natural gas pipelineoperation management

First level Second level

Strategicrisk(C1)[30ndash32]

Macro environment(C11) [33ndash35]Industry environment(C12) [36]

Internal resources andcapabilities(C13) [37]

Marketrisk(C2)[38ndash40]

Downstream user(C21) [41]Imbalance between supply and

demand(C22)Price(C23)

Competition(C24)

Financialrisk(C3)[42 43]

Debt paying ability(C31)Operating ability(C32) [43ndash51]Profitability ability(C33) [52]Development ability(C34) [53]

Operationrisk(C4)[26 54 55]

Human resources(C41) [56ndash59]Material purchase (C42) [60ndash62]

Management system(C43)HSE(C44) [63ndash65]

Legalrisk(C5) [63]

Contract(C51) [66 67]Safety and environmental

protection law(C52) [11 68ndash70]Labor Law(C53)

Operating compliance(C54)

312 Industry and Competition Risk In most countries thenatural gas industry is monopolized [36] The transportationand sales of natural gas are controlled by the natural gaspipeline company The alternative pipeline transportationproduct such as LNG photovoltaic and wind energy willbreak the monopoly Meanwhile with the introducing ofnew competitors companies will have to reduce margins toparticipate in competitions

313 Internal Resources and Capacity Risk Strategic plan-ning implementation investment decision-making andoperational capability have influence on the internalresources and capacity risk [37] It is mainly reflected in theunreasonable strategic decision-making market positioningis not accurate and the development strategy is not clearThe mismatch between the organizational structure andoperation mode and the development strategy will lead tothe results that the implementation of the strategy is blockedand the strategic awareness of all staff in the organization isweak and the planning and execution of the unit cannot becarried out in good accordance with the corporate strategy

32 Market Risk Market risk refers to the interactionbetween participants in the market competition whichcauses the change of supply and demand in the market andthe uncertainty of this change forms the market risk [38]

The market risk factors of the enterprise can be summedup in four aspects downstream user risk unbalanced market

supply and demand risk market price risk and competitionrisk [39 40]

321 Downstream User Risk Downstream natural gas pipe-line users include industrial users city gas LNG and CNG[41] Downstream user risk is mainly reflected in the gasconsumption instability and downstream users stickinessinstability The instability of the gas season will bring unbal-anced supply andmarketing risks to the upstream explorationand development and midstream pipeline transportationenterprises In the precondition that the gas has no muchmore price advantage compared with the oil coal and othertraditional energy fuel the cost of equipment replacementand branch line construction will reduce the customer stick-iness

322 The Imbalance between Supply and Demand RisksThe relationship between supply and demand in the marketdetermines the position of buyers and sellers in the marketFor example in this country with undeveloped industry theaverage daily gas consumption is much less than the optimaldesigned annual gas transmission volume As a result thenew developed natural gas field is overproduction forming abuyerrsquos market and the profit margins of upstream gas fieldsandmidstream processing plants and pipeline enterprises aredeclining

323 Price Risk The major income resource of the naturalgas pipeline companies is pipeline transportation fee Thepricing mechanism of natural gas prices mainly refer to theprice of natural gas and natural gas substitute fuels suchas oil prices in the international market and then calculatethe domestic various types of rates tax rates and upstreammining costs and profits to make a comprehensive pricingWith the increase of postproduction costs and operatingcosts natural gas prices will be raised accordingly Gas powerplants ceramics factories and cement plants are sensitive toprice changes Natural gas prices rise will inevitably lead tothe loss of some customers

324 Competition Risk The market competition risks ofnatural gas pipeline enterprises are mainly reflected in thecompetition risks of the main alternative energy utilizationand competitor risks The technological development andprice changes of alternative energy sources will directlycompete with the consumption of natural gas With thedevelopment of technologies such as LNG and LPG moreand more countries export LNG and LPG and LNG andLPG in the international market will also compete withtheir own countriesrsquo natural gas enterprises Meanwhile theother natural gas pipeline will also compete with pipelinecompanies for customer resources

33 Financial Risk Corporate financial risk refers to the riskof financial loss caused by the lack of financial rules andregulations financial operations and fund management andother noncompliance The financial risk includes corporatesolvency capital operation ability corporate profitability andsustainable development capacity aspects [42 43]

4 Mathematical Problems in Engineering

331 Corporate Solvency Risk The liquidity ratio and thequick ratio reflect the ability of the enterprise to repay thecurrent debt in the short term and they are respectivelyequal to the circulating assets and the ratio of the quickassets to the cash liabilities in a financial period Theratio of firmrsquos equity reflects the strength and weakness ofthe stability of the basic financial structure from a singleperspective Interest guarantee rate is used to measure theprofitability of the enterprise in the current period to repayinterest The cash flow debt ratio of a company is used tomeasure the current ability of an enterprise to repay short-term debts Long-term asset suitability of enterprises canbe used to measure the strength of long-term corporatesolvency

332 Enterprise Operational Capacity Risk The analysis ofthe risk of an enterprisersquos operational capability can be startedby calculating the financial indicators related to the turnoverof the enterprisersquos capital [43ndash45] Enterprise accounts receiv-able [46] turnover rate can be used to measure the flowof corporate accounts receivable The enterprise inventoryturnover rate [47] can be used tomeasure the turnover rate ofthe enterprise inventory Liquidity [48] turnover can be usedto measure the profitability of liquid assets in the enterpriseThe total asset turnover [49] of an enterprise can be used tomeasure the rate at which an enterprise invests its assets intooutput during the business cycle Nonperforming assets ratio[50] refers to the ratio of nonperforming assets to the totalassets of an enterprise in a financial period which can be usedtomeasure the nonperforming assets of enterprisesThe assetloss [51] rate refers to the ratio of the loss on fixed assets to thebeginning of the total fixed assets in a financial period whichreflects the loss of fixed assets of the enterprise

333 Enterprise Profitability Risk The analysis of corporateprofitability risk can be measured from the following indica-tors [52] Operating profit margins measure howmuch profita business unit makes Return on total assets can measure thesize of the companyrsquos assets operating efficiency Capital rateof return can reflect the ability of its own capital to obtain netincomeThe cost-of-business profit marginmeasures the costof investing the profit per unit of business

334 Enterprise Development Capability Risk Analysis ofenterprise development capability risk can start with thefollowing indicators Sales growth rate refers to the ratioof sales revenue growth and total sales revenue in the lastfinancial cycle in a financial cycle It is an important indicatorto reflect whether the enterprise is in good condition and themarket possession ability is strong or weak [53]

34 Operational Risk Operational risks of natural gas pipe-line operation management are classified into human re-source risk material procurement management risk man-agement system risk and HSE risk [26 54 55]

341 Human Resource Risk Human resources risk is mainlyin the following areas [56ndash58] The unqualified person may

be recruited into the enterprise due to the asymmetricinformation in the labor market or the unclear job demandanalysis of the recruiters The loss of staff wastage occurswhen the employeersquos contribution to the enterprise has notreached the capital cost invested by the enterprise Employeesneglect their duties abuse their powers or violate the enter-prise management system to cause financial and reputationlosses to the enterprise Staff injuries cause the enterpriseincome loss and expenses Human resource managersrsquo lack-ing of ability and management ethics causes the managerialcorruption [59]

342 Material Procurement Risk Thematerial procurementis mainly reflected in three aspects procurement cost pro-curement quality and procurement time [60] When it isimpossible to purchase materials and spare parts for thedaily operation and maintenance of the pipeline at home theenterprises must invite tenders fromabroad for procurementThe procurement cost risk is incurred in international pro-curement due to price fluctuation exchange rate fluctuationand default of bidders Purchasing quality is influencedby suppliersrsquo noncompliance of supplier quality incompletequality supervision and unqualified quality inspection Theprocurement time also has influence on the risk since the pro-duction operation is blocked due to the poor procurementthe equipment operation risk increases and the emergencyrequirement cannot be satisfied [61 62]

343 Management System Risk The risk of managementsystem ismainly manifested in the fact that due to this naturalgas pipeline management company just taking over the oper-ation from the construction parties various managementsystems in daily operation and management have not beenestablished yet It will lead to risks such as unscrupulousoperation inefficiency and poor risk control

344 HSE Risk HSE (Health Safety and Environment)[63] risk refers to the risk caused by the HSE managementwhich is not in place and HSE management system is notestablished or not completed HSE budget is insufficientSafety hazards may not be rectified timely Many other factorsalso have influence on the HSE risk such as lacking of staffsafety education fatal accidents pipeline explosion leakageaccidents and employee occupational injuries [64 65]

35 Legal Risk Natural gas transmission pipeline has longdistance and wide coverage The relationship between enter-prises and local governments is complicated There are con-tract risks legal risks of safety and environmental protectionlabor law risks and operation compliance risks in operationof natural gas pipeline [63]

351 Contract Risk The contract risk is reflected in thesigning and fulfillment of the contract [66] The naturalgas pipeline company is in the middle position in the oiland gas industry chain and plays a role of transformationTherefore many contracts and market transactions existbetween pipeline companies and upstream gas fields anddownstream users Once the contract is formulated and

Mathematical Problems in Engineering 5

implemented the enterprise will face the risk of breach ofcontract [67]

352 Safety and Environmental Legal Risks Natural gas pipe-line transportation industry belongs to the high-risk industry[11 68] The massive natural gas leakage will cause somepollution to the environment [69 70] The high-pressurenatural gas pipeline passes through the villages and citiesalong the pipeline route Once the leakage explosion andfire accidents occur the consequences will be disastrousTherefore the pipeline safety management is under strictsupervision of local authorities

353 Labor Law Risks The operation of pipelines employsmany workers and has diversified employment patternsThere are protection mechanisms for employeesrsquo rightsEspecially with the improvement of the education level of thestaff the awareness of rights protection is very strong

354 Operation Compliance Risk Operation compliancerisks are mainly reflected in the business-related licenses orqualifications that have not been obtained for a businessThere is no synchronization with the latest laws regulationsor industry standards promulgated by the state Companymay be charged with illegal business operations and so on

4 Method to Deal with the LinguisticEvaluation Information in IntuitionisticFuzzy Linguistic Environment

Let 119864 = 1198901 1198902 119890119904 be the set of evaluators 119898119894 be thealternative and 119862 = 1198621 cup 1198622 cup sdot sdot sdot cup 119862119897 = 1198881 1198882 119888119899 bethe criteria The weight vector of the evaluator obtained bysubjective weighting method is 120582 = (1205821 1205822 120582119904)119879 with thecondition 0 le 120582119896 le 1 119896 = 1 2 119904 and sum119904119896=1 120582119896 = 1

Evaluators 119890119896 evaluate the alternative 119898119894 according to thecriterion 119888119895 to obtain the intuitionistic fuzzy decision matrix119877119896 = (120572119896119894119895)119899times119898 under the attribute of the alternative in which120572119896119894119895 = (120583119896119894119895 V119896119894119895) 0 le 120583119896119894119895 le 1 0 le V119896119894119895 le 1 0 le 120583119896119894119895+V119896119894119895 le1 120587119896119894119895 = 1 minus 120583119896119894119895 minus V119896119894119895 and 120583119896119894119895 V119896119894119895 120587119896119894119895 respectively expresses thesatisfaction dissatisfaction and hesitancy degree of evaluator119890119896 under the attributes 119888119895 of the alternative 119898119894

The higher the consistency of expertsrsquo opinion on eval-uation of criteria weight the greater the weight of expertsin weight evaluation [71] More experts agree on the criteriaweight and risk assessment the more understanding of thecriteria and the enterprise risk the greater the weight of theexperts in the risk assessment In the following based on thework [23ndash25 71 72] the linguistic evaluation information isdealt with by the following steps

Step 1 Assume that the rating of alternative mi with respectto criterion 119888119895 given by the evaluators 119890119896 can be expressedin IFS 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) the rating of importance ofcriterion 119888119895 given by the evaluators119890119896 can be expressed inIFS 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 ) Therefore the corresponding

intuitionistic fuzzy decision matrices 119877(119896) and 119882(119896) can beexpressed in matrix form concisely as follows

119877(119896) = (119903(119896)119894119895 )119899times119898 =[[[[[[[[

119903(119896)11 119903(119896)12 sdot sdot sdot 119903(119896)1119899119903(119896)21 119903(119896)22 sdot sdot sdot 119903(119896)2119899 119903(119896)1198981 119903(119896)1198982 sdot sdot sdot 119903(119896)119898119899

]]]]]]]]

119882(119896) = (119908(119896)1 119908(119896)2 119908(119896)119899 )

(10)

where 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 )Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21According to (3) construct the aggregated intuitionistic

fuzzy decision matrix 119875 = (1205751 1205752 120575119899) based on theimportance of criteria by

120575119895 = 119904⨁119896=1

119908(119896)119895 120582119896 = (120583119895 V119895) (11)

120583119894 = 1 minus 119904prod119896=1

(1 minus 120583(119896)119895 )119908(119896)119895

V119896119894 =119904prod119896=1

(V(119896)119895 )119908(119896)119895(12)

Step 22According to (3) construct the initial aggregated intu-

itionistic fuzzy decision matrix 119876 = (120572119894119895)119899times119898 based on therating of alternative by

120572119894119895 = 119904⨁119870=1

119903119896119894119895120582119896 = (120583119894119895 V119894119895)

= (1 minus 119904prod119896=1

(1 minus 120583119896119894119895)120582119896 119904prod119896=1

(V119896119894119895)120582119896)(13)

Step 3 Obtain the entropy weightStep 31Based on (8) obtain the entropy weight119867119888(119890119896) of evalua-

tors 119890119896 on the evaluation of criteria by

119864119896119888 = 1n

119899sum119894=1

[120587(119896)119895 + radic22 (1 minus 120587(119896)119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119895 minus V(119896)11989510038161003816100381610038161003816] (14)

119867119888 (119890119896) = (1 minus 119864119896119888)(119904 minus sum119904119896=1 119864119896119888 ) (15)

6 Mathematical Problems in Engineering

Step 32Based on (8) obtain the entropy weight 119867119898119894(119890119896) of

evaluators on the evaluation of alternative 119898119894 by119864119896119894 = 1

n

119899sum119894=1

[120587(119896)119894119895 + radic22 (1 minus 120587(119896)119894119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119894119895 minus V11989611989411989510038161003816100381610038161003816] (16)

119867119898119894 (119890119896) = (1 minus 119864119896119894 )(119904 minus sum119904119896=1 119864119896119894 ) (17)

Step 4 Calculate the cross entropy weightStep 41Based on (9) Calculate the cross entropy weight119862119888(119890119896) of

evaluator 119890119896 on the evaluation of criteria using

119863119888 (119884119896119888 119883119888) = 119899sum119895=1

[[120583(119896)119895 ln

120583(119896)119895(12) (120583(119896)119895 +120583119895)

+ V(119896)119894119895 lnV(119896)119895

(12) (V(119896)119895 +V119895)]]+ 119899sum119895=1

[[120583119895 ln 120583119895

(12) (120583(119896)119895 +120583119895)+ V119895 ln

V119895(12) (V(119896)119895 +V119895)]]

(18)

119862119888 (119890119896) = 1119863119888 (119884119896119888 119883119888)sum119904119896=1 (1119863119888 (119884119896119888 119883119888)) (19)

Step 42Based on (9) calculate the cross entropy weight119862119898119894(119890119896) on the evaluation of alternative 119898119894 using119863119898119894

(119884119896119898119894 119883119898119894) =119899sum119895=1

[[120583(119896)119894119895 ln

120583(119896)119894119895(12) (120583(119896)119894119895 +120583119894119895)

+ V(119896)119894119895 lnV(119896)119894119895

(12) (V(119896)119894119895 +V119894119895)]]+ 119899sum119895=1

[[120583119894119895 ln 120583119894119895

(12) (120583(119896)119894119895 +120583119894119895)+ V119894119895 ln

V119894119895(12) (V(119896)119894119895 +V119894119895)]]

(20)

119862119898119894 (119890119896) = 1119863119898119894 (119884119896119898119894 119883119898119894)sum119904119896=1 (1119863119898119894 (119884119896119898119894 119883119898119894)) (21)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights of evaluator 119890119896

Step 51According to the cross entropy weights and entropy

weights of evaluator 119890119896 on the evaluation of importance ofcriteria we can get the corresponding final weight 120578119896119888 by

120578119896119888 = 120572119862119888 (119890119896) + (1 minus 120572)119867119888 (119890119896) (22)

where 0 le 120572 le 1Step 52According to the cross entropy weights and entropy

weights of evaluators on the evaluation of alternative 119898119894 wecan get the corresponding final weight 120578119870119894 by

120578119896119894 = 120572119862119898119894 (119890119896) + (1 minus 120572)119867119898119894 (119890119896) (23)

where 0 le 120572 le 1Step 53Based on 120578119896119888 and 120578119896119894 calculate the corresponding final

weight 120578119896 which comprehensively considers the consistencyof indicator evaluation and the indicator scores of evaluatorsby

120578119896 = 120579120578119896119888 + (1 minus 120579) 120578119896119894 (24)

where 0 le 120579 le 1Step 6 Calculate the integrated evaluation result matrix

Step 61Integrating the weight based on the rating of criteria

of evaluators based on (4) the integrated evaluation resultmatrix 119861119888 = (1205731 1205732 120573119899) of criteria can be obtained by

120573119895 = 119904sum119896=1

119908(119896)119895 120578119896119888 = (120588119895 120590119895) (25)

Step 62Integrating the weight based on both the rating of

alternatives and the rating of criteria of evaluators based on(4) the integrated evaluation result matrix 119861119898119894 = (120573119894119895)119899times119898 ofalternative 119898119894 is derived by

120573119894119895 = 119904sum119896=1

120572(119896)119894119895 120578119896 = (120588119894119895 120590119894119895) (26)

Step 7 Determine the final evaluation results Based on(2)ndash(6) the weighted evaluation value of the alternative 119898119894with respect to the parent criteria 119862119897 can be derived by

120585119894119897 = sum119888119895isin119862119897 120573119894119895 otimes 120573119895sum119888119895isin119862119897 120573119895 = sum119888119895isin119862119897 (120588119894119895 120590119894119895) otimes (120588119895 120590119895)sum119888119895isin119862119897 (120588119895 120590119895)= (120588119894119897 120590119894119897)

(27)

Step 8 In the condition that the weighted rating of thealternative with the parent criteria needs to be comparedbased on (7) the score function can be used as

119904 (120585119894119897) = 119896120588119894119897 minus 119896120590119894119897 + (1 minus 2119896) 10038161003816100381610038161 minus (120588119894119897 + 120590119894119897)1003816100381610038161003816 (28)

Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

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Submit your manuscripts atwwwhindawicom

4 Mathematical Problems in Engineering

331 Corporate Solvency Risk The liquidity ratio and thequick ratio reflect the ability of the enterprise to repay thecurrent debt in the short term and they are respectivelyequal to the circulating assets and the ratio of the quickassets to the cash liabilities in a financial period Theratio of firmrsquos equity reflects the strength and weakness ofthe stability of the basic financial structure from a singleperspective Interest guarantee rate is used to measure theprofitability of the enterprise in the current period to repayinterest The cash flow debt ratio of a company is used tomeasure the current ability of an enterprise to repay short-term debts Long-term asset suitability of enterprises canbe used to measure the strength of long-term corporatesolvency

332 Enterprise Operational Capacity Risk The analysis ofthe risk of an enterprisersquos operational capability can be startedby calculating the financial indicators related to the turnoverof the enterprisersquos capital [43ndash45] Enterprise accounts receiv-able [46] turnover rate can be used to measure the flowof corporate accounts receivable The enterprise inventoryturnover rate [47] can be used tomeasure the turnover rate ofthe enterprise inventory Liquidity [48] turnover can be usedto measure the profitability of liquid assets in the enterpriseThe total asset turnover [49] of an enterprise can be used tomeasure the rate at which an enterprise invests its assets intooutput during the business cycle Nonperforming assets ratio[50] refers to the ratio of nonperforming assets to the totalassets of an enterprise in a financial period which can be usedtomeasure the nonperforming assets of enterprisesThe assetloss [51] rate refers to the ratio of the loss on fixed assets to thebeginning of the total fixed assets in a financial period whichreflects the loss of fixed assets of the enterprise

333 Enterprise Profitability Risk The analysis of corporateprofitability risk can be measured from the following indica-tors [52] Operating profit margins measure howmuch profita business unit makes Return on total assets can measure thesize of the companyrsquos assets operating efficiency Capital rateof return can reflect the ability of its own capital to obtain netincomeThe cost-of-business profit marginmeasures the costof investing the profit per unit of business

334 Enterprise Development Capability Risk Analysis ofenterprise development capability risk can start with thefollowing indicators Sales growth rate refers to the ratioof sales revenue growth and total sales revenue in the lastfinancial cycle in a financial cycle It is an important indicatorto reflect whether the enterprise is in good condition and themarket possession ability is strong or weak [53]

34 Operational Risk Operational risks of natural gas pipe-line operation management are classified into human re-source risk material procurement management risk man-agement system risk and HSE risk [26 54 55]

341 Human Resource Risk Human resources risk is mainlyin the following areas [56ndash58] The unqualified person may

be recruited into the enterprise due to the asymmetricinformation in the labor market or the unclear job demandanalysis of the recruiters The loss of staff wastage occurswhen the employeersquos contribution to the enterprise has notreached the capital cost invested by the enterprise Employeesneglect their duties abuse their powers or violate the enter-prise management system to cause financial and reputationlosses to the enterprise Staff injuries cause the enterpriseincome loss and expenses Human resource managersrsquo lack-ing of ability and management ethics causes the managerialcorruption [59]

342 Material Procurement Risk Thematerial procurementis mainly reflected in three aspects procurement cost pro-curement quality and procurement time [60] When it isimpossible to purchase materials and spare parts for thedaily operation and maintenance of the pipeline at home theenterprises must invite tenders fromabroad for procurementThe procurement cost risk is incurred in international pro-curement due to price fluctuation exchange rate fluctuationand default of bidders Purchasing quality is influencedby suppliersrsquo noncompliance of supplier quality incompletequality supervision and unqualified quality inspection Theprocurement time also has influence on the risk since the pro-duction operation is blocked due to the poor procurementthe equipment operation risk increases and the emergencyrequirement cannot be satisfied [61 62]

343 Management System Risk The risk of managementsystem ismainly manifested in the fact that due to this naturalgas pipeline management company just taking over the oper-ation from the construction parties various managementsystems in daily operation and management have not beenestablished yet It will lead to risks such as unscrupulousoperation inefficiency and poor risk control

344 HSE Risk HSE (Health Safety and Environment)[63] risk refers to the risk caused by the HSE managementwhich is not in place and HSE management system is notestablished or not completed HSE budget is insufficientSafety hazards may not be rectified timely Many other factorsalso have influence on the HSE risk such as lacking of staffsafety education fatal accidents pipeline explosion leakageaccidents and employee occupational injuries [64 65]

35 Legal Risk Natural gas transmission pipeline has longdistance and wide coverage The relationship between enter-prises and local governments is complicated There are con-tract risks legal risks of safety and environmental protectionlabor law risks and operation compliance risks in operationof natural gas pipeline [63]

351 Contract Risk The contract risk is reflected in thesigning and fulfillment of the contract [66] The naturalgas pipeline company is in the middle position in the oiland gas industry chain and plays a role of transformationTherefore many contracts and market transactions existbetween pipeline companies and upstream gas fields anddownstream users Once the contract is formulated and

Mathematical Problems in Engineering 5

implemented the enterprise will face the risk of breach ofcontract [67]

352 Safety and Environmental Legal Risks Natural gas pipe-line transportation industry belongs to the high-risk industry[11 68] The massive natural gas leakage will cause somepollution to the environment [69 70] The high-pressurenatural gas pipeline passes through the villages and citiesalong the pipeline route Once the leakage explosion andfire accidents occur the consequences will be disastrousTherefore the pipeline safety management is under strictsupervision of local authorities

353 Labor Law Risks The operation of pipelines employsmany workers and has diversified employment patternsThere are protection mechanisms for employeesrsquo rightsEspecially with the improvement of the education level of thestaff the awareness of rights protection is very strong

354 Operation Compliance Risk Operation compliancerisks are mainly reflected in the business-related licenses orqualifications that have not been obtained for a businessThere is no synchronization with the latest laws regulationsor industry standards promulgated by the state Companymay be charged with illegal business operations and so on

4 Method to Deal with the LinguisticEvaluation Information in IntuitionisticFuzzy Linguistic Environment

Let 119864 = 1198901 1198902 119890119904 be the set of evaluators 119898119894 be thealternative and 119862 = 1198621 cup 1198622 cup sdot sdot sdot cup 119862119897 = 1198881 1198882 119888119899 bethe criteria The weight vector of the evaluator obtained bysubjective weighting method is 120582 = (1205821 1205822 120582119904)119879 with thecondition 0 le 120582119896 le 1 119896 = 1 2 119904 and sum119904119896=1 120582119896 = 1

Evaluators 119890119896 evaluate the alternative 119898119894 according to thecriterion 119888119895 to obtain the intuitionistic fuzzy decision matrix119877119896 = (120572119896119894119895)119899times119898 under the attribute of the alternative in which120572119896119894119895 = (120583119896119894119895 V119896119894119895) 0 le 120583119896119894119895 le 1 0 le V119896119894119895 le 1 0 le 120583119896119894119895+V119896119894119895 le1 120587119896119894119895 = 1 minus 120583119896119894119895 minus V119896119894119895 and 120583119896119894119895 V119896119894119895 120587119896119894119895 respectively expresses thesatisfaction dissatisfaction and hesitancy degree of evaluator119890119896 under the attributes 119888119895 of the alternative 119898119894

The higher the consistency of expertsrsquo opinion on eval-uation of criteria weight the greater the weight of expertsin weight evaluation [71] More experts agree on the criteriaweight and risk assessment the more understanding of thecriteria and the enterprise risk the greater the weight of theexperts in the risk assessment In the following based on thework [23ndash25 71 72] the linguistic evaluation information isdealt with by the following steps

Step 1 Assume that the rating of alternative mi with respectto criterion 119888119895 given by the evaluators 119890119896 can be expressedin IFS 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) the rating of importance ofcriterion 119888119895 given by the evaluators119890119896 can be expressed inIFS 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 ) Therefore the corresponding

intuitionistic fuzzy decision matrices 119877(119896) and 119882(119896) can beexpressed in matrix form concisely as follows

119877(119896) = (119903(119896)119894119895 )119899times119898 =[[[[[[[[

119903(119896)11 119903(119896)12 sdot sdot sdot 119903(119896)1119899119903(119896)21 119903(119896)22 sdot sdot sdot 119903(119896)2119899 119903(119896)1198981 119903(119896)1198982 sdot sdot sdot 119903(119896)119898119899

]]]]]]]]

119882(119896) = (119908(119896)1 119908(119896)2 119908(119896)119899 )

(10)

where 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 )Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21According to (3) construct the aggregated intuitionistic

fuzzy decision matrix 119875 = (1205751 1205752 120575119899) based on theimportance of criteria by

120575119895 = 119904⨁119896=1

119908(119896)119895 120582119896 = (120583119895 V119895) (11)

120583119894 = 1 minus 119904prod119896=1

(1 minus 120583(119896)119895 )119908(119896)119895

V119896119894 =119904prod119896=1

(V(119896)119895 )119908(119896)119895(12)

Step 22According to (3) construct the initial aggregated intu-

itionistic fuzzy decision matrix 119876 = (120572119894119895)119899times119898 based on therating of alternative by

120572119894119895 = 119904⨁119870=1

119903119896119894119895120582119896 = (120583119894119895 V119894119895)

= (1 minus 119904prod119896=1

(1 minus 120583119896119894119895)120582119896 119904prod119896=1

(V119896119894119895)120582119896)(13)

Step 3 Obtain the entropy weightStep 31Based on (8) obtain the entropy weight119867119888(119890119896) of evalua-

tors 119890119896 on the evaluation of criteria by

119864119896119888 = 1n

119899sum119894=1

[120587(119896)119895 + radic22 (1 minus 120587(119896)119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119895 minus V(119896)11989510038161003816100381610038161003816] (14)

119867119888 (119890119896) = (1 minus 119864119896119888)(119904 minus sum119904119896=1 119864119896119888 ) (15)

6 Mathematical Problems in Engineering

Step 32Based on (8) obtain the entropy weight 119867119898119894(119890119896) of

evaluators on the evaluation of alternative 119898119894 by119864119896119894 = 1

n

119899sum119894=1

[120587(119896)119894119895 + radic22 (1 minus 120587(119896)119894119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119894119895 minus V11989611989411989510038161003816100381610038161003816] (16)

119867119898119894 (119890119896) = (1 minus 119864119896119894 )(119904 minus sum119904119896=1 119864119896119894 ) (17)

Step 4 Calculate the cross entropy weightStep 41Based on (9) Calculate the cross entropy weight119862119888(119890119896) of

evaluator 119890119896 on the evaluation of criteria using

119863119888 (119884119896119888 119883119888) = 119899sum119895=1

[[120583(119896)119895 ln

120583(119896)119895(12) (120583(119896)119895 +120583119895)

+ V(119896)119894119895 lnV(119896)119895

(12) (V(119896)119895 +V119895)]]+ 119899sum119895=1

[[120583119895 ln 120583119895

(12) (120583(119896)119895 +120583119895)+ V119895 ln

V119895(12) (V(119896)119895 +V119895)]]

(18)

119862119888 (119890119896) = 1119863119888 (119884119896119888 119883119888)sum119904119896=1 (1119863119888 (119884119896119888 119883119888)) (19)

Step 42Based on (9) calculate the cross entropy weight119862119898119894(119890119896) on the evaluation of alternative 119898119894 using119863119898119894

(119884119896119898119894 119883119898119894) =119899sum119895=1

[[120583(119896)119894119895 ln

120583(119896)119894119895(12) (120583(119896)119894119895 +120583119894119895)

+ V(119896)119894119895 lnV(119896)119894119895

(12) (V(119896)119894119895 +V119894119895)]]+ 119899sum119895=1

[[120583119894119895 ln 120583119894119895

(12) (120583(119896)119894119895 +120583119894119895)+ V119894119895 ln

V119894119895(12) (V(119896)119894119895 +V119894119895)]]

(20)

119862119898119894 (119890119896) = 1119863119898119894 (119884119896119898119894 119883119898119894)sum119904119896=1 (1119863119898119894 (119884119896119898119894 119883119898119894)) (21)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights of evaluator 119890119896

Step 51According to the cross entropy weights and entropy

weights of evaluator 119890119896 on the evaluation of importance ofcriteria we can get the corresponding final weight 120578119896119888 by

120578119896119888 = 120572119862119888 (119890119896) + (1 minus 120572)119867119888 (119890119896) (22)

where 0 le 120572 le 1Step 52According to the cross entropy weights and entropy

weights of evaluators on the evaluation of alternative 119898119894 wecan get the corresponding final weight 120578119870119894 by

120578119896119894 = 120572119862119898119894 (119890119896) + (1 minus 120572)119867119898119894 (119890119896) (23)

where 0 le 120572 le 1Step 53Based on 120578119896119888 and 120578119896119894 calculate the corresponding final

weight 120578119896 which comprehensively considers the consistencyof indicator evaluation and the indicator scores of evaluatorsby

120578119896 = 120579120578119896119888 + (1 minus 120579) 120578119896119894 (24)

where 0 le 120579 le 1Step 6 Calculate the integrated evaluation result matrix

Step 61Integrating the weight based on the rating of criteria

of evaluators based on (4) the integrated evaluation resultmatrix 119861119888 = (1205731 1205732 120573119899) of criteria can be obtained by

120573119895 = 119904sum119896=1

119908(119896)119895 120578119896119888 = (120588119895 120590119895) (25)

Step 62Integrating the weight based on both the rating of

alternatives and the rating of criteria of evaluators based on(4) the integrated evaluation result matrix 119861119898119894 = (120573119894119895)119899times119898 ofalternative 119898119894 is derived by

120573119894119895 = 119904sum119896=1

120572(119896)119894119895 120578119896 = (120588119894119895 120590119894119895) (26)

Step 7 Determine the final evaluation results Based on(2)ndash(6) the weighted evaluation value of the alternative 119898119894with respect to the parent criteria 119862119897 can be derived by

120585119894119897 = sum119888119895isin119862119897 120573119894119895 otimes 120573119895sum119888119895isin119862119897 120573119895 = sum119888119895isin119862119897 (120588119894119895 120590119894119895) otimes (120588119895 120590119895)sum119888119895isin119862119897 (120588119895 120590119895)= (120588119894119897 120590119894119897)

(27)

Step 8 In the condition that the weighted rating of thealternative with the parent criteria needs to be comparedbased on (7) the score function can be used as

119904 (120585119894119897) = 119896120588119894119897 minus 119896120590119894119897 + (1 minus 2119896) 10038161003816100381610038161 minus (120588119894119897 + 120590119894119897)1003816100381610038161003816 (28)

Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

Mathematical Problems in Engineering 5

implemented the enterprise will face the risk of breach ofcontract [67]

352 Safety and Environmental Legal Risks Natural gas pipe-line transportation industry belongs to the high-risk industry[11 68] The massive natural gas leakage will cause somepollution to the environment [69 70] The high-pressurenatural gas pipeline passes through the villages and citiesalong the pipeline route Once the leakage explosion andfire accidents occur the consequences will be disastrousTherefore the pipeline safety management is under strictsupervision of local authorities

353 Labor Law Risks The operation of pipelines employsmany workers and has diversified employment patternsThere are protection mechanisms for employeesrsquo rightsEspecially with the improvement of the education level of thestaff the awareness of rights protection is very strong

354 Operation Compliance Risk Operation compliancerisks are mainly reflected in the business-related licenses orqualifications that have not been obtained for a businessThere is no synchronization with the latest laws regulationsor industry standards promulgated by the state Companymay be charged with illegal business operations and so on

4 Method to Deal with the LinguisticEvaluation Information in IntuitionisticFuzzy Linguistic Environment

Let 119864 = 1198901 1198902 119890119904 be the set of evaluators 119898119894 be thealternative and 119862 = 1198621 cup 1198622 cup sdot sdot sdot cup 119862119897 = 1198881 1198882 119888119899 bethe criteria The weight vector of the evaluator obtained bysubjective weighting method is 120582 = (1205821 1205822 120582119904)119879 with thecondition 0 le 120582119896 le 1 119896 = 1 2 119904 and sum119904119896=1 120582119896 = 1

Evaluators 119890119896 evaluate the alternative 119898119894 according to thecriterion 119888119895 to obtain the intuitionistic fuzzy decision matrix119877119896 = (120572119896119894119895)119899times119898 under the attribute of the alternative in which120572119896119894119895 = (120583119896119894119895 V119896119894119895) 0 le 120583119896119894119895 le 1 0 le V119896119894119895 le 1 0 le 120583119896119894119895+V119896119894119895 le1 120587119896119894119895 = 1 minus 120583119896119894119895 minus V119896119894119895 and 120583119896119894119895 V119896119894119895 120587119896119894119895 respectively expresses thesatisfaction dissatisfaction and hesitancy degree of evaluator119890119896 under the attributes 119888119895 of the alternative 119898119894

The higher the consistency of expertsrsquo opinion on eval-uation of criteria weight the greater the weight of expertsin weight evaluation [71] More experts agree on the criteriaweight and risk assessment the more understanding of thecriteria and the enterprise risk the greater the weight of theexperts in the risk assessment In the following based on thework [23ndash25 71 72] the linguistic evaluation information isdealt with by the following steps

Step 1 Assume that the rating of alternative mi with respectto criterion 119888119895 given by the evaluators 119890119896 can be expressedin IFS 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) the rating of importance ofcriterion 119888119895 given by the evaluators119890119896 can be expressed inIFS 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 ) Therefore the corresponding

intuitionistic fuzzy decision matrices 119877(119896) and 119882(119896) can beexpressed in matrix form concisely as follows

119877(119896) = (119903(119896)119894119895 )119899times119898 =[[[[[[[[

119903(119896)11 119903(119896)12 sdot sdot sdot 119903(119896)1119899119903(119896)21 119903(119896)22 sdot sdot sdot 119903(119896)2119899 119903(119896)1198981 119903(119896)1198982 sdot sdot sdot 119903(119896)119898119899

]]]]]]]]

119882(119896) = (119908(119896)1 119908(119896)2 119908(119896)119899 )

(10)

where 119903(119896)119894119895 = (120583(119896)119894119895 V(119896)119894119895 120587(119896)119894119895 ) 119908(119896)119895 = (120583(119896)119895 V(119896)119895 120587(119896)119895 )Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21According to (3) construct the aggregated intuitionistic

fuzzy decision matrix 119875 = (1205751 1205752 120575119899) based on theimportance of criteria by

120575119895 = 119904⨁119896=1

119908(119896)119895 120582119896 = (120583119895 V119895) (11)

120583119894 = 1 minus 119904prod119896=1

(1 minus 120583(119896)119895 )119908(119896)119895

V119896119894 =119904prod119896=1

(V(119896)119895 )119908(119896)119895(12)

Step 22According to (3) construct the initial aggregated intu-

itionistic fuzzy decision matrix 119876 = (120572119894119895)119899times119898 based on therating of alternative by

120572119894119895 = 119904⨁119870=1

119903119896119894119895120582119896 = (120583119894119895 V119894119895)

= (1 minus 119904prod119896=1

(1 minus 120583119896119894119895)120582119896 119904prod119896=1

(V119896119894119895)120582119896)(13)

Step 3 Obtain the entropy weightStep 31Based on (8) obtain the entropy weight119867119888(119890119896) of evalua-

tors 119890119896 on the evaluation of criteria by

119864119896119888 = 1n

119899sum119894=1

[120587(119896)119895 + radic22 (1 minus 120587(119896)119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119895 minus V(119896)11989510038161003816100381610038161003816] (14)

119867119888 (119890119896) = (1 minus 119864119896119888)(119904 minus sum119904119896=1 119864119896119888 ) (15)

6 Mathematical Problems in Engineering

Step 32Based on (8) obtain the entropy weight 119867119898119894(119890119896) of

evaluators on the evaluation of alternative 119898119894 by119864119896119894 = 1

n

119899sum119894=1

[120587(119896)119894119895 + radic22 (1 minus 120587(119896)119894119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119894119895 minus V11989611989411989510038161003816100381610038161003816] (16)

119867119898119894 (119890119896) = (1 minus 119864119896119894 )(119904 minus sum119904119896=1 119864119896119894 ) (17)

Step 4 Calculate the cross entropy weightStep 41Based on (9) Calculate the cross entropy weight119862119888(119890119896) of

evaluator 119890119896 on the evaluation of criteria using

119863119888 (119884119896119888 119883119888) = 119899sum119895=1

[[120583(119896)119895 ln

120583(119896)119895(12) (120583(119896)119895 +120583119895)

+ V(119896)119894119895 lnV(119896)119895

(12) (V(119896)119895 +V119895)]]+ 119899sum119895=1

[[120583119895 ln 120583119895

(12) (120583(119896)119895 +120583119895)+ V119895 ln

V119895(12) (V(119896)119895 +V119895)]]

(18)

119862119888 (119890119896) = 1119863119888 (119884119896119888 119883119888)sum119904119896=1 (1119863119888 (119884119896119888 119883119888)) (19)

Step 42Based on (9) calculate the cross entropy weight119862119898119894(119890119896) on the evaluation of alternative 119898119894 using119863119898119894

(119884119896119898119894 119883119898119894) =119899sum119895=1

[[120583(119896)119894119895 ln

120583(119896)119894119895(12) (120583(119896)119894119895 +120583119894119895)

+ V(119896)119894119895 lnV(119896)119894119895

(12) (V(119896)119894119895 +V119894119895)]]+ 119899sum119895=1

[[120583119894119895 ln 120583119894119895

(12) (120583(119896)119894119895 +120583119894119895)+ V119894119895 ln

V119894119895(12) (V(119896)119894119895 +V119894119895)]]

(20)

119862119898119894 (119890119896) = 1119863119898119894 (119884119896119898119894 119883119898119894)sum119904119896=1 (1119863119898119894 (119884119896119898119894 119883119898119894)) (21)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights of evaluator 119890119896

Step 51According to the cross entropy weights and entropy

weights of evaluator 119890119896 on the evaluation of importance ofcriteria we can get the corresponding final weight 120578119896119888 by

120578119896119888 = 120572119862119888 (119890119896) + (1 minus 120572)119867119888 (119890119896) (22)

where 0 le 120572 le 1Step 52According to the cross entropy weights and entropy

weights of evaluators on the evaluation of alternative 119898119894 wecan get the corresponding final weight 120578119870119894 by

120578119896119894 = 120572119862119898119894 (119890119896) + (1 minus 120572)119867119898119894 (119890119896) (23)

where 0 le 120572 le 1Step 53Based on 120578119896119888 and 120578119896119894 calculate the corresponding final

weight 120578119896 which comprehensively considers the consistencyof indicator evaluation and the indicator scores of evaluatorsby

120578119896 = 120579120578119896119888 + (1 minus 120579) 120578119896119894 (24)

where 0 le 120579 le 1Step 6 Calculate the integrated evaluation result matrix

Step 61Integrating the weight based on the rating of criteria

of evaluators based on (4) the integrated evaluation resultmatrix 119861119888 = (1205731 1205732 120573119899) of criteria can be obtained by

120573119895 = 119904sum119896=1

119908(119896)119895 120578119896119888 = (120588119895 120590119895) (25)

Step 62Integrating the weight based on both the rating of

alternatives and the rating of criteria of evaluators based on(4) the integrated evaluation result matrix 119861119898119894 = (120573119894119895)119899times119898 ofalternative 119898119894 is derived by

120573119894119895 = 119904sum119896=1

120572(119896)119894119895 120578119896 = (120588119894119895 120590119894119895) (26)

Step 7 Determine the final evaluation results Based on(2)ndash(6) the weighted evaluation value of the alternative 119898119894with respect to the parent criteria 119862119897 can be derived by

120585119894119897 = sum119888119895isin119862119897 120573119894119895 otimes 120573119895sum119888119895isin119862119897 120573119895 = sum119888119895isin119862119897 (120588119894119895 120590119894119895) otimes (120588119895 120590119895)sum119888119895isin119862119897 (120588119895 120590119895)= (120588119894119897 120590119894119897)

(27)

Step 8 In the condition that the weighted rating of thealternative with the parent criteria needs to be comparedbased on (7) the score function can be used as

119904 (120585119894119897) = 119896120588119894119897 minus 119896120590119894119897 + (1 minus 2119896) 10038161003816100381610038161 minus (120588119894119897 + 120590119894119897)1003816100381610038161003816 (28)

Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

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Submit your manuscripts atwwwhindawicom

6 Mathematical Problems in Engineering

Step 32Based on (8) obtain the entropy weight 119867119898119894(119890119896) of

evaluators on the evaluation of alternative 119898119894 by119864119896119894 = 1

n

119899sum119894=1

[120587(119896)119894119895 + radic22 (1 minus 120587(119896)119894119895 ) minus radic22 10038161003816100381610038161003816120583(119896)119894119895 minus V11989611989411989510038161003816100381610038161003816] (16)

119867119898119894 (119890119896) = (1 minus 119864119896119894 )(119904 minus sum119904119896=1 119864119896119894 ) (17)

Step 4 Calculate the cross entropy weightStep 41Based on (9) Calculate the cross entropy weight119862119888(119890119896) of

evaluator 119890119896 on the evaluation of criteria using

119863119888 (119884119896119888 119883119888) = 119899sum119895=1

[[120583(119896)119895 ln

120583(119896)119895(12) (120583(119896)119895 +120583119895)

+ V(119896)119894119895 lnV(119896)119895

(12) (V(119896)119895 +V119895)]]+ 119899sum119895=1

[[120583119895 ln 120583119895

(12) (120583(119896)119895 +120583119895)+ V119895 ln

V119895(12) (V(119896)119895 +V119895)]]

(18)

119862119888 (119890119896) = 1119863119888 (119884119896119888 119883119888)sum119904119896=1 (1119863119888 (119884119896119888 119883119888)) (19)

Step 42Based on (9) calculate the cross entropy weight119862119898119894(119890119896) on the evaluation of alternative 119898119894 using119863119898119894

(119884119896119898119894 119883119898119894) =119899sum119895=1

[[120583(119896)119894119895 ln

120583(119896)119894119895(12) (120583(119896)119894119895 +120583119894119895)

+ V(119896)119894119895 lnV(119896)119894119895

(12) (V(119896)119894119895 +V119894119895)]]+ 119899sum119895=1

[[120583119894119895 ln 120583119894119895

(12) (120583(119896)119894119895 +120583119894119895)+ V119894119895 ln

V119894119895(12) (V(119896)119894119895 +V119894119895)]]

(20)

119862119898119894 (119890119896) = 1119863119898119894 (119884119896119898119894 119883119898119894)sum119904119896=1 (1119863119898119894 (119884119896119898119894 119883119898119894)) (21)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights of evaluator 119890119896

Step 51According to the cross entropy weights and entropy

weights of evaluator 119890119896 on the evaluation of importance ofcriteria we can get the corresponding final weight 120578119896119888 by

120578119896119888 = 120572119862119888 (119890119896) + (1 minus 120572)119867119888 (119890119896) (22)

where 0 le 120572 le 1Step 52According to the cross entropy weights and entropy

weights of evaluators on the evaluation of alternative 119898119894 wecan get the corresponding final weight 120578119870119894 by

120578119896119894 = 120572119862119898119894 (119890119896) + (1 minus 120572)119867119898119894 (119890119896) (23)

where 0 le 120572 le 1Step 53Based on 120578119896119888 and 120578119896119894 calculate the corresponding final

weight 120578119896 which comprehensively considers the consistencyof indicator evaluation and the indicator scores of evaluatorsby

120578119896 = 120579120578119896119888 + (1 minus 120579) 120578119896119894 (24)

where 0 le 120579 le 1Step 6 Calculate the integrated evaluation result matrix

Step 61Integrating the weight based on the rating of criteria

of evaluators based on (4) the integrated evaluation resultmatrix 119861119888 = (1205731 1205732 120573119899) of criteria can be obtained by

120573119895 = 119904sum119896=1

119908(119896)119895 120578119896119888 = (120588119895 120590119895) (25)

Step 62Integrating the weight based on both the rating of

alternatives and the rating of criteria of evaluators based on(4) the integrated evaluation result matrix 119861119898119894 = (120573119894119895)119899times119898 ofalternative 119898119894 is derived by

120573119894119895 = 119904sum119896=1

120572(119896)119894119895 120578119896 = (120588119894119895 120590119894119895) (26)

Step 7 Determine the final evaluation results Based on(2)ndash(6) the weighted evaluation value of the alternative 119898119894with respect to the parent criteria 119862119897 can be derived by

120585119894119897 = sum119888119895isin119862119897 120573119894119895 otimes 120573119895sum119888119895isin119862119897 120573119895 = sum119888119895isin119862119897 (120588119894119895 120590119894119895) otimes (120588119895 120590119895)sum119888119895isin119862119897 (120588119895 120590119895)= (120588119894119897 120590119894119897)

(27)

Step 8 In the condition that the weighted rating of thealternative with the parent criteria needs to be comparedbased on (7) the score function can be used as

119904 (120585119894119897) = 119896120588119894119897 minus 119896120590119894119897 + (1 minus 2119896) 10038161003816100381610038161 minus (120588119894119897 + 120590119894119897)1003816100381610038161003816 (28)

Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

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[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

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[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

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[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

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[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

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Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

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[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

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Mathematical Problems in Engineering 7

Table 2 Linguistic term sets

Linguistic terms forweight of criteria

Linguistic termsfor risk level IFNs

Very importance(VI) Very high(VH) (090 005 005)Importance(I) High(H) (075 020 005)Medium(F) Medium(F) (050 040 010)Unimportance(U) Low(L) (025 060015)Veryunimportance(VU) Very low(VL) (010 080 010)

5 Illustrate Example

In order to control the risks of X Gas Pipeline Company theproposed approach is used to evaluate the risks Seven peopleare invited to evaluate the risk of company and the weight ofcriteria using the linguistic terms in Table 2

51 Deriving the Evaluation Results

Step 1 Construct the individual intuitionistic fuzzy decisionmatrix

Using Table 2 the initial ratings are converted into theintuitionistic fuzzy forms in Tables 3 and 4

Step 2 Construct the initial aggregated intuitionistic fuzzydecision matrix separately

Step 21Weights of the experts are equal then with (11)-(12) the

matrix can be derived as is in the fourth column of Table 5Step 22Weights of the experts are equal with (13) the initial

aggregated intuitionistic fuzzy decision matrix of risk levelof the company can be derived as in the second column ofTable 5

Step 3 Obtain the entropy weightStep 31The entropy weights of evaluators based on the evaluation

of the criteria can be calculated with (14)-(15)

119867119888 (1198901) = 019119867119888 (1198902) = 019119867119888 (1198903) = 011119867119888 (1198904) = 012119867119888 (1198905) = 012119867119888 (1198906) = 013119867119888 (1198907) = 012

(29)

Step 32The entropy weights of evaluators based on the evaluation

of the company can be calculated with (16)-(17)

119867119909 (1198901) = 013

119867119909 (1198902) = 014119867119909 (1198903) = 015119867119909 (1198904) = 017119867119909 (1198905) = 014119867119909 (1198906) = 013119867119909 (1198907) = 014

(30)

Step 4 Calculate the cross entropy weightStep 41With (18)-(19) the cross entropy weight of evaluators

based on evaluation of the criteria can be derived119862119888 (1198901)=014119862119888 (1198902) = 010119862119888 (1198903) = 019119862119888 (1198904) = 012119862119888 (1198905) = 016119862119888 (1198906) = 013119862119888 (1198907) = 016

(31)

Step 42With (20)-(21) the cross entropy weight of evaluators

based on evaluation of the company can be derived

119862119909 (1198901) = 014119862119909 (1198902) = 019119862119909 (1198903) = 014119862119909 (1198904) = 010119862119909 (1198905) = 015119862119909 (1198906) = 013119862119909 (1198907) = 015

(32)

Step 5 Attain the corresponding final weight based on thecross entropy weights and entropy weights

Step 51Let 120572 = 06 120573 = 04 then using (22) the final weight of

evaluators based on the evaluation of criteria can be gotten

1205781119888 = 0161205782119888 = 0141205783119888 = 0161205784119888 = 0121205785119888 = 015

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

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Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

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Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

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Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

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Submit your manuscripts atwwwhindawicom

8 Mathematical Problems in Engineering

Table3Intuition

isticfuzzydecisio

nmatric

esof

weight

ofcriteria

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(050040

010

)C 12

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 13

(090005005)

(075020005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(050040

010

)C 21

(075020005)

(010

080010)

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

C 22

(050040

010

)(025060

015

)(025060

015

)(025060

015

)(050040

010

)(010

080010)

(050040

010

)C 23

(075020005)

(010

080010)

(050040

010

)(050040

010

)(050040

010

)(050040

010

)(025060

015

)C 24

(090005005)

(090005005)

(025060

015

)(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 31

(075020005)

(025060

015

)(050040

010

)(010

080010)

(075020005)

(050040

010

)(075020005)

C 32

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 33

(090005005)

(075020005)

(025060

015

)(050040

010

)(050040

010

)(025060

015

)(050040

010

)C 34

(090005005)

(090005005)

(050040

010

)(050040

010

)(075020005)

(075020005)

(050040

010

)C 41

(075020005)

(090005005)

(050040

010

)(050040

010

)(050040

010

)(075020005)

(075020005)

C 42

(075020005)

(025060

015

)(050040

010

)(050040

010

)(050040

010

)(050040

010

)(075020005)

C 43

(090005005)

(090005005)

(075020005)

(050040

010

)(025060

015

)(075020005)

(050040

010

)C 44

(090005005)

(090005005)

(075020005)

(050040

010

)(075020005)

(075020005)

(075020005)

C 51

(090005005)

(090005005)

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

C 52

(090005005)

(090005005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(090005005)

(090005005)

(075020005)

(075020005)

(025060

015

)(050040

010

)(025060

015

)C 54

(090005005)

(090005005)

(050040

010

)(075020005)

(050040

010

)(025060

015

)(075020005)

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

Mathematical Problems in Engineering 9

Table4Th

eIntuitio

nisticfuzzy

decisio

nmatric

esof

risklevelofthe

company

e 1e 2

e 3e 4

e 5e 6

e 7C 11

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

C 12

(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)(025060

015

)C 13

(050040

010

)(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

(075020005)

C 21

(010

080010)

(025060

015

)(075020005)

(010

080010)

(025060

015

)(025060

015

)(050040

010

)C 22

(010

080010)

(025060

015

)(025060

015

)(010

080010)

(025060

015

)(010

080010)

(050040

010

)C 23

(025060

015

)(025060

015

)(075020005)

(010

080010)

(010

080010)

(050040

010

)(025060

015

)C 24

(050040

010

)(075020005)

(025060

015

)(010

080010)

(010

080010)

(025060

015

)(025060

015

)C 31

(050040

010

)(050040

010

)(025060

015

)(090005005)

(090005005)

(050040

010

)(075020005)

C 32

(050040

010

)(050040

010

)(050040

010

)(075020005)

(025060

015

)(025060

015

)(050040

010

)C 33

(050040

010

)(050040

010

)(025060

015

)(025060

015

)(050040

010

)(025060

015

)(050040

010

)C 34

(050040

010

)(050040

010

)(090005005)

(025060

015

)(050040

010

)(075020005)

(025060

015

)C 41

(025060

015

)(075020005)

(050040

010

)(050040

010

)(025060

015

)(075020005)

(050040

010

)C 42

(025060

015

)(025060

015

)(050040

010

)(090005005)

(025060

015

)(025060

015

)(090005005)

C 43

(075020005)

(050040

010

)(075020005)

(075020005)

(050040

010

)(075020005)

(025060

015

)C 44

(025060

015

)(090005005)

(075020005)

(090005005)

(050040

010

)(025060

015

)(075020005)

C 51

(050040

010

)(075020005)

(050040

010

)(010

080010)

(075020005)

(025060

015

)(050040

010

)C 52

(025060

015

)(075020005)

(075020005)

(025060

015

)(075020005)

(050040

010

)(025060

015

)C 53

(025060

015

)(050040

010

)(075020005)

(025060

015

)(050040

010

)(050040

010

)(010

080010)

C 54

(025060

015

)(010

080010)

(010

080010)

(010

080010)

(010

080010)

(050040

010

)(010

080010)

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

10 Mathematical Problems in Engineering

Table 5 Intuitionistic fuzzy decision matrixes

CriteriaInitial

aggregated risklevel of thecompany

Ratings of thecompany with

weight ofexperts

Initialaggregatedweight ofcriteria

Weightedratings ofcriteria

C11 (019 070) (019 070) (070 022) (070 022)C12 (025 060) (025 060) (047 042) (047 042)C13 (072 022) (072 022) (067 024) (068 024)C21 (036 053) (037 052) (059 033) (059 033)C22 (024 064) (024 064) (035 053) (036 052)C23 (036 053) (037 052) (048 042) (049 042)C24 (036 053) (037 052) (059 029) (061 028)C31 (070 021) (069 022) (057 035) (059 033)C32 (049 041) (049 041) (062 029) (062 028)C33 (041 048) (041 047) (060 030) (061 029)C34 (060 030) (060 030) (074 018) (075 018)C41 (054 037) (054 037) (070 022) (070 022)C42 (060 028) (059 030) (057 035) (057 034)C43 (064 020) (064 030) (073 019) (073 019)C44 (071 038) (071 020) (079 015) (079 014)C51 (053 035) (054 037) (079 015) (079 015)C52 (056 035) (057 035) (071 020) (072 019)C53 (045 050) (045 045) (071 020) (071 020)C54 (019 070) (019 070) (073 019) (073 019)

1205786119888 = 0131205787119888 = 015

(33)

Step 52Let 120572 = 06 120573 = 04 then using (23) the final weight

of evaluators based on the evaluation of evaluation of thecompany can be gotten

1205781119909 = 0131205782119909 = 0171205783119909 = 0141205784119909 = 0131205785119909 = 0151205786119909 = 0131205787119909 = 014

(34)

Step 53Let 120579 = 05 based on 120578119896119888 and 120578119896119894 calculate the corre-

sponding final weight 120578119896 which comprehensively considersthe consistency of evaluation indicators of evaluators and theindicator scores by (24)

1205781 = 015

1205782 = 0151205783 = 0151205784 = 0121205785 = 0151205786 = 0131205787 = 014

(35)

Step 6 Calculate the integrated evaluation result matrixStep 61The weighted evaluation result of the criteria can be

derived with (25) The results are shown in the fifth columnof Table 5

Step 62The ratings of risk level of the company integrated with

weight of experts can be derived with (26) The results areshown in the third column of Table 5

Step 7 and 8Let 119896 = 05 By using (27)-(28) the weighted evaluation

value of the company with respect to the parent criteria andthe corresponding score value can be derived which areshown in Table 6

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

Mathematical Problems in Engineering 11

Table 6 Weighted evaluation value of the company in the first level

Criteria Weighted evaluation value Score value RankStrategic risk (061 024) 019 FourthMarket risk (055 026) 014 FifthFinancial risk (083 007) 038 SecondOperation risk (090 004) 043 FirstLegal risk (080 009) 035 Third

Overall the risk of gas pipeline operation management isnot high Among the risks the operation risk is higher thanother risks and market risk is lowest

6 Conclusions

As the risks control is important for safeguarding the opera-tion of natural gas pipelines In the paper the evaluation of therisk of natural gas pipeline operation management is studiedFirstly the comprehensive evaluation criteria for the risk ofnatural gas pipeline operation management are constructedIt is constructed from the strategic risk market risk financialrisk operation risk and legal risk aspects Since the ratingsare given in linguistic terms In order to deal with theseratings the intuitionistic fuzzy model is used to represent thelinguistic terms The illustrated example shows the proposedapproach is feasible

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China under Grants 71571191 and 91646122the Humanity and Social Science Youth Foundation of Min-istry of Education in China (Project nos 15YJCZH081 and13YJC790112) the Science Foundation of China University ofPetroleum Beijing (no 2462015YQ0722) andBeijingNaturalScience Foundation (no 9162003)

References

[1] A Correlje and C van der Linde ldquoEnergy supply security andgeopolitics A European perspectiverdquo Energy Policy vol 34 no5 pp 532ndash543 2006

[2] W Z KhanM YAalsalemWGharibi andQArshad ldquoOil andgas monitoring using wireless sensor networks requirementsissues and challengesrdquo in Proceedings of the 2016 Interna-tional Conference on Radar Antenna Microwave Electronicsand Telecommunications ICRAMET 2016 pp 31ndash35 JakartaIndonesia October 2016

[3] P K Dey ldquoAnalytic hierarchy process analyzes risk of operatingcross-country petroleum pipelines in indiardquo Natural HazardsReview vol 4 no 4 pp 213ndash221 2003

[4] P K Dey ldquoManaging project risk using combined analytichierarchy process and risk maprdquo Applied Soft Computing vol10 no 4 pp 990ndash1000 2010

[5] S Karimi R Sam J Balist and S M Bigdeli ldquoPipeline riskassessment by using hazard risk assessment (HAZOP)Method(Case study Gas transmission Pipeline Of Savadkooh Iran)rdquoEnvironmental Science An Indian Journal vol 12 no 3 2016

[6] Z Zhou J Gong A Roda and K Farrag ldquoMultiresolutionchange analysis framework for postdisaster assessment of natu-ral gas pipeline riskrdquo Transportation Research Record vol 2595pp 29ndash39 2016

[7] M Bai Y Du Y Chen Y Xing and P Zhao ldquoRisk assessmentof long gas and oil pipeline projects inducing landslide disastersduring constructionrdquo Journal of Performance of ConstructedFacilities vol 31 no 5 Article ID 04017063 2017

[8] J Soszynska ldquoReliability and risk evaluation of a port oilpipeline transportation system in variable operation condi-tionsrdquo International Journal of Pressure Vessels and Piping vol87 no 2-3 pp 81ndash87 2010

[9] Z Y Han and W G Weng ldquoComparison study on qualitativeand quantitative risk assessment methods for urban natural gaspipeline networkrdquo Journal of Hazardous Materials vol 189 no1-2 pp 509ndash518 2011

[10] A Jamshidi A Yazdani-Chamzini S H Yakhchali and SKhaleghi ldquoDeveloping a new fuzzy inference system forpipeline risk assessmentrdquo Journal of Loss Prevention in theProcess Industries vol 26 no 1 pp 197ndash208 2013

[11] Y-D Jo and J A Bum ldquoA method of quantitative risk assess-ment for transmission pipeline carrying natural gasrdquo Journal ofHazardous Materials vol 123 no 1-3 pp 1ndash12 2005

[12] H S Tooranloo and A S Ayatollah ldquoA model for failure modeand effects analysis based on intuitionistic fuzzy approachrdquoApplied Soft Computing vol 49 pp 238ndash247 2016

[13] M Li and C Wu ldquoA distance model of intuitionistic fuzzycross entropy to solve preference problem on alternativesrdquoMathematical Problems in Engineering vol 2016 Article ID8324124 9 pages 2016

[14] F Wang S Zeng and C Zhang ldquoA method based on intu-itionistic fuzzy dependent aggregation operators for supplierselectionrdquo Mathematical Problems in Engineering vol 2013Article ID 481202 9 pages 2013

[15] C Chen ldquoExtensions of the TOPSIS for group decision-makingunder fuzzy environmentrdquo Fuzzy Sets and Systems vol 114 no1 pp 1ndash9 2000

[16] W-FDai Q-Y Zhong andC-Z Qi ldquoMultistagemultiattributegroup decision-making method based on triangular fuzzyMULTIMOORArdquo Mathematical Problems in Engineering vol2016 Article ID 1687068 8 pages 2016

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

12 Mathematical Problems in Engineering

[17] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986

[18] Z Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEE Trans-actions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007

[19] C Kahraman S CevikOnar S Cebi andBOztaysi ldquoExtensionof information axiom from ordinary to intuitionistic fuzzy setsAn application to search algorithm selectionrdquo Computers ampIndustrial Engineering vol 105 pp 348ndash361 2017

[20] Y Zhang Y Zhang Y Li S Liu and J Yang ldquoA study of rurallogistics center location based on intuitionistic fuzzy TOPSISrdquoMathematical Problems in Engineering vol 2017 Article ID2323057 7 pages 2017

[21] J Guo ldquoHybrid multicriteria group decision making methodfor information system project selection based on intuitionisticfuzzy theoryrdquoMathematical Problems in Engineering vol 2013Article ID 859537 12 pages 2013

[22] K Govindan R Khodaverdi and A Vafadarnikjoo ldquoIntu-itionistic fuzzy based DEMATEL method for developing greenpractices and performances in a green supply chainrdquo ExpertSystems with Applications vol 42 no 20 pp 7207ndash7220 2015

[23] I K Vlachos and G D Sergiadis ldquoIntuitionistic fuzzy informa-tionmdashapplications to pattern recognitionrdquo Pattern RecognitionLetters vol 28 no 2 pp 197ndash206 2007

[24] L Z Song G Peng and W Y Yang ldquoExtension factor forsurfaces with maximum in region experimentsrdquo ComputerEngineering and Applications vol 45 no 7 pp 187ndash189 2009(Chinese)

[25] C Wang D Yao J Mao and L Sun ldquoIntuitionistic fuzzymultiple attributes decision making method based on entropyand correlation coefficientrdquo Journal of Computer Applicationsvol 32 no 11 pp 3002ndash3004 2012

[26] Z Yanjiao Research of the internal control risk classificationmanagement based on sinopec pipeline storage and transporta-tion company [MS thesis] Yangtze University Hubei China2015

[27] T Wei ldquoConstruction of legal risk and prevention and controlmechanism for long-distance natural gas pipeline enterprisesrdquoOriental Enterprise Culture vol 10 article 165 2014

[28] S Wei Research oil evaluation of risk management in LiaohePetroleum [MS thesis] DalianUniversity ofTechnology Liaon-ing China 2008

[29] C Jichao Construction and Application of Operational RiskAssessment Model for Oilfield Enterprises Based on Factor Anal-ysis Method Southwest Petroleum University Sichuan China2014

[30] Z Aimin H Wang and L Ningning ldquoAnalysis of the factorsand formation mechanism of corporate strategic riskrdquo ModernManagement Science vol 7 pp 64-65 2006

[31] L Jiequn and Z Qing ldquoA review of corporate strategic riskresearchrdquo Productivity Research no 2 pp 239ndash241 2010

[32] Y Q Shang Research on the identification evaluation andresponse of enterprise strategic risk [PhD thesis] Capital Uni-versity of Economics and Business Beijing China 2011

[33] Q Han L Chen and Y Liu ldquoTanzaniarsquos oil and gas investmentenvironmentrdquo International Petroleum Economics vol Z1 pp133ndash139 2014

[34] L Tian Z Wang A Krupnick and X Liu ldquoStimulatingshale gas development in China A comparison with the USexperiencerdquo Energy Policy vol 75 pp 109ndash116 2014

[35] R E Ericson ldquoEurasian natural gas pipelines The politicaleconomy of network interdependencerdquo Eurasian Geographyand Economics vol 50 no 1 pp 28ndash57 2009

[36] R J Pierce ldquoReconsidering the Roles of Regulation and Com-petition in the Natural Gas IndustryrdquoHarvard Law Review vol97 no 2 p 345 1983

[37] B Quelin and F Duhamel ldquoBringing together strategic out-sourcing and corporate strategy Outsourcing motives andrisksrdquoEuropeanManagement Journal vol 21 no 5 pp 647ndash6612003

[38] W Liu Research on risk early warning mechanism of naturalgas market in XX company [MS thesis] Southwest PetroleumUniversity Chengdu China 2016

[39] Y Liu S Li andX Xu ldquoResearches on risk evaluationmodels ofnatural gas industry downstreammarketrdquoNatural Gas Industryvol 26 no 7 pp A20ndash138 2006

[40] H Olin and W Xiaoming ldquoSafety of natural gas supply andcountermeasuresrdquoNatural Gas Industry vol 28 no 10 pp 125ndash129 2008

[41] N Higashi Energy Markets and Security Working Papers IEAEnergy Markets and Security Working Papers 2009

[42] Y C Feng Research on financial risk control of overseas of Aproject in J Oilfield [MS thesis] China University of Petroleum(Beijing) Beijing China 2013

[43] D X Cheng et al The Theory and Practice of The FinancialAnalysis and Evaluation of Oil Enterprises Petroleum IndustryPress 2010

[44] L L Wang ldquoProblems and Countermeasures in the manage-ment of enterprise inventoryrdquo Friends of Accountants vol 35pp 72-73 2011

[45] X Y Qian ldquoAn analysis of the combination of accountreceivable turnover and inventory turnoverrdquo in Financial andAccounting Monthly Comprehensive Edition vol 7 pp 80ndash822012

[46] K Padachi ldquoTrends in working capital management and itsimpact on firmsrsquo performance an analysis of Mauritian smallmanufacturing firmsrdquo International Review of Business ResearchPapers vol 2 no 2 pp 45ndash58 2006

[47] L M Hitt D J Wu and X Zhou ldquoInvestment in enterpriseresource planning Business impact and productivitymeasuresrdquoJournal of Management Information Systems vol 19 no 1 pp71ndash98 2002

[48] M Baker and J C Stein ldquoMarket liquidity as a sentimentindicatorrdquo Journal of FinancialMarkets vol 7 no 3 pp 271ndash2992004

[49] P M Fairfield and T L Yohn ldquoUsing asset turnover andprofit margin to forecast changes in profitabilityrdquo Review ofAccounting Studies vol 6 no 4 pp 371ndash385 2001

[50] K Kaur and B Singh ldquoon-performing assets of public andprivate sector banks (a comparative study)rdquo South Asian Journalof Marketing amp Management Research vol 1 no 3 pp 54ndash722011

[51] J McPeak ldquoConfronting the risk of asset loss What roledo livestock transfers in northern Kenya playrdquo Journal ofDevelopment Economics vol 81 no 2 pp 415ndash437 2006

[52] J Jiao ldquoStudy on the index system of evaluating the quality ofthe listed companiesrsquo profitrdquo Enterpriser World vol 2 pp 216-217 2010

[53] A M Qian and X M Zhang ldquoConstruction and testing ofthree-dimensional composite evaluation system of enterprisesrsquofinancial situationmdashevidence from Chinarsquos a share listed com-panies in manufacturing industryrdquo China Industrial Economicsvol 276 pp 88-89 2011

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

Mathematical Problems in Engineering 13

[54] W Xuxiang T Lizhong and L Guoxiang ldquoResearch on enter-prise operational risk managementrdquo China Market vol 1 pp48-49 2008

[55] W Wenshuai ldquoDiscussion on enterprise operation cost man-agement and risk controlrdquo Technology and Enterprise vol 5 p16 2012

[56] J Xin and Y Naiding ldquoThe risk study of human resourcemanagementrdquo Soft Science vol 17 no 6 pp 53ndash55 2003

[57] Z Mingjia ldquoThe warning index system of risk in humanresource managementrdquo Journal of Xirsquoan Institute of Technologyvol 24 no 4 pp 403ndash406 2004

[58] S Zehou and L Dongmei ldquoRisk management in humanresource managementrdquo Human Resources Development inChina vol 9 pp 32ndash35 2002

[59] C H Shen Research on the analysis evaluation and Counter-measures of human resource risk [MS thesis]WuhanUniversityof Technology Wuhan China 2003

[60] Y Dong ldquoEnterprise material procurement risk and its man-agement preventionrdquoContemporary Economy vol 21 pp 22-232010

[61] J Hui ldquoHow to prevent the risk of material procurementrdquoFinancial Management vol 6 pp 62ndash64 2005

[62] Y Qiu Identification and control of material purchase risk ofoverseas of A project [MS thesis] East China University ofScience and Technology Shanghai China 2016

[63] H Tang ldquoConstruction of legal risk and prevention and controlmechanism for the long distance pipeline enterprise of naturalgasrdquo Oriental Enterprise Culture vol 10 pp 180ndash183 2014

[64] GChenY ShupingGQingyu L Jian andX Ling ldquoIntegratedHSE-risk management mode of long distance transmissionpipeline construction projectsrdquo Industrial Engineering andManagement vol 14 no 5 pp 129ndash134 2009

[65] L J Jiang Analysis of HSE management system in Chinarsquospetroleum enterprises [MS thesis] China University ofPetroleum (Beijing) Beijing China 2011

[66] F K Adams ldquoConstruction contract risk management a studyof practices in the United Kingdomrdquo Cost Engineering vol 50no 1 pp 22ndash33 2008

[67] J H Barton ldquoThe economic basis of damages for breach ofcontractrdquoThe Journal of Legal Studies vol 1 no 2 pp 277ndash3041972

[68] A J Brito andAT deAlmeida ldquoMulti-attribute risk assessmentfor risk ranking of natural gas pipelinesrdquo Reliability Engineeringamp System Safety vol 94 no 2 pp 187ndash198 2009

[69] I Dincer and M A Rosen ldquoEnergy environment and sustain-able developmentrdquoApplied Energy vol 64 no 1ndash4 pp 427ndash4401999

[70] D J Rozell and S J Reaven ldquoWater pollution risk associatedwith natural gas extraction from the Marcellus Shalerdquo RiskAnalysis vol 32 no 8 pp 1382ndash1393 2012

[71] M Li W Wei J Wang and X Qi ldquoApproach to evaluatingaccounting informatization based on entropy in intuitionisticfuzzy environmentrdquo Entropy vol 20 no 6 article 476 2018

[72] X JWang C PWei andT TGuo ldquoAmethod to derive expertsrsquoweights in intuitionistic fuzzy multi-attribute group decisionmaking based on cross entropy and entropyrdquo Journal of QufuNormal University (Natural Science) vol 37 no 3 2011

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom