Decision Making on Analyzing Advanced Manufacturing Systems

Dimensions: SWARA and COPRAS_G Integration (Case Study:

Automotive Industry)

Gholamreza Jamali, KH.Farrokhnejad, Masoumeh Mohammadi Abstract The main purpose of this study is to analyze and evaluate dimensions of Advanced Manufacturing Systems (AMS) using SWARA and COPRAS_G Integration in the automotive industry. First, according to the research literature and 5 automotive industry experts’ opinion, six criteria include flexibility, productivity, inventory, operating cost, responsibility and reconfiguration indicated and weighted by Stepwise Weight Assessment Ratio Analysis (SWARA) technique. Then, four AMS introduced as alternatives include Computer Numerical Control (CNC), Flexible Manufacturing System (FMS), Computer Integrated Manufacturing Systems (CIM) and Humanized Flexible Manufacturing System (HFMS) were ranked using Complex Proportional Assessment of alternatives-Grey (COPRAS_G). Results showed that, HFMS, FMS, CNC and CIMS were ranked first and fourth respectively. So for the heavy industries such as automotive industry it is important the linkage the automation process with human interval and relation between planning and flexible manufacturing integration toward advanced potential advantages. Key words: AMS, COPRAS-GREY, HFMS, SWARA.

© 2015 BBT Pub. All rights reserved.

Introduction Manufacturing industries have become more competitive since middle of the 1960s. In order to survive in more aggressive environment, industries initially focused on reducing costs, and subsequently changed their target on improving quality and recently on customization. Huge competition within the manufacturing industries due to globalization has put up tremendous pressure on them to deliver products in shorter time with the desired quality. Developments in the field of production technology that have been made across the world in past few years have made production much more versatile. Changing life style and availability of many alternative products have reduced the product life cycles, Therefore, industries need to be more capable to manufacture diverse ranges of products in variable batch sizes and keep the manufacturing facilities ready for new components in minimum changeover time. Industries are motivated to attain these capabilities through automation, robotics, computer numerically controlled (CNC) machines and other modern concepts, like FMS (Karande and Chakraborty, 2013). FMS involves use of automated guided vehicles (AGVs), computer numerical controls (CNCs) and other automation techniques to provide flexibility in operations, machines, products, quality and process (Dubey and Ali, 2014). FMSs are highly automated production systems, consisting of a computer-controlled integrated configuration of multipurpose workstations, storage buffers and one or more AGVs (Novas and Henning, 2014). Flexibility is an important feature to accommodate changes in the operating environment and is an adaptive response to unpredictable situations. Therefore, flexibility has now become a key factor in manufacturing organizations (Wahab and Osman, 2013). Stockton and Bateman (1995) have suggested that flexibility is the ability of a manufacturing system to: change between existing part types, change the operation routes of components, change the operations required to process a component, change production volumes, i.e. either expansion or contraction, add new part types, add new processes to the system (Jain and Raj, 2013). Several researchers have classified flexibility under different categories in table 1.

Table 1: Represents Criteria of Flexibility in FMS from Viewpoints of Distinguished Scholars CRITERIA REFERENCE

Machine, process, operation, material handling, tool, product, and market

Wahab and Osman (2013)

machine, process, routing, operation, product, volume, expansion and production

Browne et al., (1984), Singholi et al., (2013), Jain and Raj (2013)

machine, process, routing, operation, product, volume, expansion, and short-medium-term

Barad and Sipper (1988), Karsak (2002)

routing, process, product, production, volume and expansion Azzone and Berteles (1989), Karsak (2002), Jain and Raj (2013)

process, product, demand and equipment Park and Son (1988), and Son and Park (1990), Jain and Raj (2013)

product, process, program, production, volume, routing, expansion, operation, machine, material handling and market

Sethi and Sethi (1990), Jain and Raj (2013)

machine, routing, process, product, volume, material handling, operation, expansion, production, program, response, product mix,

size, range

Jain and Raj (2013)

G.Jamali et.al / Teknologi Tanaman /Vol (12), Supp (2) 2015 267

The adoption and implementation of new and latest manufacturing systems has been the most important issue in the manufacturing world. The various AMSs like CNCs, computer integrated manufacturing (CIM), FMS are very well known manufacturing systems and are these used frequently in the developed countries. However, the developing countries have shown reluctance in adopting these systems especially FMS. The simple reason is that FMS uses robots and AGV’S for material handling; however the human labour is cheaply and easily available in these countries. Therefore HFMS has been proposed by the authors in place of FMS, which surely takes care of human element. HFMS has been found advantageous over CNCs, CIM and FMS on the basis of flexibility, reconfigurability, inventory, operating cost, responsiveness and productivity (Nagar and Raj, 2012). The concept of HFMS is very well illustrated in the next sections. The main objectives of this paper are as follows: To propose an entirely new manufacturing system, i.e. HFMS To identify and discuss various attributes and sub-attributes of four types of alternatives such as CNC, FMS, CIM and HFMS To select the best alternative using SWARA and COPRAS-GREY. To discuss the implications of this research and suggest directions for future research

The main objective of this research paper is to justify the investment in a new kind of manufacturing system, i.e. HFMS highlighting its characteristics among other advanced manufacturing technologies (AMTs) like CNCs, FMS and CIM through a decision making process. Due to the complexity in the decision process involving interacting elements, a decision-making approach is required to evaluate the objectives, attributes and feasible alternatives of a new manufacturing system. In these research two MCDM methods including SWARA and COPRAS are applied for AMS. SWARA will use for evaluating and calculating weights and relative importance of each criterion and COPRAS will apply for evaluating alternatives of research. The process of this research is shown in Figure 1.

Figure 1. The evaluation procedure

Identification of selection criteria

Constructing decision making team

Determining qualitative and quantitative criteria

for evaluation process

Construction of selection criteria and problem

structure

Assigning evaluations via SWARA

Criteria weights by SWARA

Assigning evaluations for COPRAS

Grey computations

Ranking via COPRAS-G

Select the best AMS

Ph

ase

III

Ph

ase

II

Ph

ase

I

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Literature Review Because many studies done in the field FMSs summary of the research conducted at the table ... explained.

Table 2: Summary of the Research in the Field of FMS TITLE RESEARCH REFERENCE

A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making

Kashfi and Javadi (2015)

Approaches of fuzzy systems applied to an AGV dispatching system in a FMS

Carida et al., (2015)

Production effects by form changes of autonomous decentralized FMSs with mind

Yamamoto et al., (2015)

A Hybrid Genetic Algorithm for Simultaneous Scheduling of Machines and AGVs in FMS

Lin et al., (2015)

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and

AGVs

Sanches et al., (2015)

Identifying FMS repetitive patterns for efficient search-based scheduling algorithm: A colored Petri net approach

Baruwa and Piera (2015)

Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem

Candan and Yazgan (2015)

Implementation of an overall design of a Flexible manufacturing system

Erdin and Atmaca (2015)

Scheduling of Flexible Manufacturing Systems using Fuzzy Logic: A Review

Shrivastava (2015)

Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Rey et al (2014)

Methodology SWARA Method The step-wise weight assessment ratio analysis (SWARA) (Kersuliene et al. 2010) methodology is developed in 2010 and applied for the selection of rational dispute resolution method (Kersuliene and Turskis, 2011). In SWARA method each of experts first of all ranks criteria. The most significant criterion is given rank 1, and the least significant criterion is given rank last. The overall ranks to the group of experts are determined according to the mediocre value of ranks (Kersuliene and Turskis, 2011). The procedure for the criteria weights determination is presented in Fig. 2.

Drawing a set of criteria Responded surveyList of main criteria

Drawing general list of criteria

Arrangement of criteria according to

frequency of indication

Analysis of criteria list

Deletion of interrelated criteria

Drawing of unrelated criteria list

Determination of criteria importance vector

Determination of criteria importance

Wj=qi/∑qi

Stop

Responded survey (respondents arrange criteria according

to rank, the most important criterion being listed

as the first , etc)

Determination of criteria ranks

Determination of criteria weights

Presentation of j criterion

Evaluation of how much j +1 criterion

is must important than j criterion

Relative comparison should be applied

Value of importance

of j +1 criterion

J=j+1

Presentation of j +1

criterion

J< = n

n is number of unrelated

criteria)

YesYes

NoNo

Figure 2. Determining of the criteria weights based on SWARA (source: Kersuliene & Turskis, 2011)

The main feature of SWARA method is the possibility to estimate experts or interest groups opinion about significance ratio of the criteria in the process of their weights determination (Kersuliene et al, 2010). This method is useful for coordinating and gathering data from experts. SWARA applications are uncomplicated and experts in various fields can contact with general idea of this method easily. The recent developments of decision making models based on SWARA method are listed below:

G.Jamali et.al / Teknologi Tanaman /Vol (12), Supp (2) 2015 269

Table 3: Application Areas SWARA Method APPLICATION AREAS REFERENCE

The Selection of a Packaging Design Stanujkic et al., (2015)

Evaluation of Strategies Hashemkhani zolfani et al., (2015)

Glasshouse Locating Haghnazar Kouchaksaraei et al., (2015)

Assessment of regions priority for implementation of solar projects

Vafaeipour et al., (2014)

Evaluation of external wall insulation in Residential buildings Ruzgys et al., (2014)

Sales Branches Performance Evaluation Aghdaie et al., (2014)

Forecast market share and the possibility of retention and movement of bank customers

Jamali (2014)

COPRAS-G Method In order to evaluate the overall efficiency of a project, it is necessary to identify selection criteria, to assess information, relating to these criteria. Zavadskas et al. (2008) presented the main ideas of complex proportional assessment method with grey interval numbers (COPRAS-G) method. The idea of COPRAS-G method with criterion values expressed in intervals is based on the real conditions of decision making and applications of the Grey systems theory (Deng, 1982; Deng, 1988). The COPRAS-G method uses a stepwise ranking and evaluating procedure of the alternatives in terms of significance and utility.The recent developments of decision making models based on COPRAS and COPRAS-G method are listed below:

Table 4: Application Areas of COPRAS-G Method APPLICATION AREAS REFERENCE

Glasshouse Locating Haghnazar Kouchaksaraei et al., (2015)

Aviation Technical Publication Content Management System Selection

Skeete et al., (2015)

Material Selection Xia et al., (2015)

For improving and selecting suppliers in green supply chain management

Liou et al., (2015)

For Investment prioritizing in high tech industries Hashemkhani Zolfani And Bahrami (2014)

Sustainable Development of Rural Areas’ Building Structures Based on Local Climate

Hashemkhani Zolfani and Zavadskas (2013)

Machine Tool Selection Aghdaie et al., (2013)

The procedure of applying the COPRAS-G method consists in the following steps (Zavadskas et al. 2009): 1. Selecting the set of the most important criteria, describing the alternatives. 2. Constructing the decision-making matrix ⨂x. Here ⨂xin is determined xn1 (the smallest value, the lower limit) and ̃ ̀ (the biggest value, the upper limit).

(1)

minj

xxxxx

xxxxx

xxxxx

X

mnmnmjmjm

nnjj

nnjj

,1,,1;

],[

],[

],[

;1

2;22221

1;11111

3. Determining significances of the criteria q. 4. Normalizing the decision-making matrix ⊗X:

‎(2)

minjnj ji

xji

x

jix

,1;,1;)(

2

In formula (2) is the lower value of the i criterion in the alternative j of the solution; is the upper value of

the criterion i in the alternative j of the solution; m is the number of criteria; n is the number of the alternatives, compared.

5. Calculating the weighted normalized decision matrix ⨂ . The weighted normalized values ⨂ ji are calculated as follows:

‎ (3)

.

.

.

j i ij

j i j i i j i j i i

X x q

o r

x x q an d x x q

In formula (3), q1 is the significance of the i –th criterion. Then, the normalized decision-making matrix is: ‎ (4)

mnmnmjmjm

nnjj

nnjj

xxxxx

xxxxx

xxxxx

X

;1

2;22221

1;11111

],[

],[

],[

6. Calculating the sums P j of criterion values, whose larger values are more preferable:

270 , G.Jamali et.al / Teknologi Tanaman /Vol (12), Supp (2) 2015

‎ (5)

k

j

ijijjxxP

1

)(2

1

7. Calculating the sums R j of criterion values, whose smaller values are more preferable:

‎ (6) mkixxR

m

ki

ijijj,;)(

2

1

1

In formula (6), (m- k) is the number of criteria which must be minimized. 8. Determining the minimal value of R j as follows:

(7) nj

jijRR

,1

minmin

9. Calculating the relative significance of each alternatively Q j the expression: (8)

n

j

j

j

n

j j

jj

RP

R

pQ

1

1

1

10. Determining the optimally criterion by K the formula:

(9) nj

jjQK

,1;

max

11. Determining the priority order of the alternatives. 12. Calculating the utility degree of each alternative by the formula:

‎ (10) %100.

maxQ

QN

j

j

Here Q j and Q max are the significances of the alternatives obtained from equation. Case Study The automotive industry, after the oil industry is the largest industry in Iran. Automotive industry, including all parts of design, development, production, market and sell motor vehicle industries. The companies and factories in involved designing, manufacturing, marketing and sale of motor vehicles are part of this industry. In this research, a group of experts participate in three important parts of study. The first role of these experts is establishing a model of research includes some criterion. Evaluating criteria is the second role of the experts. They participate at this stage to solve the SWARA method. At the last step, experts evaluate the buildings structures systems based on COPRAS method. Model of Research The research model is established in a conference meeting in which experts participate. This meeting consists of two sections. In first section, important criteria are specified and in second section, after many discussions, the model of research is established.

Table 5: Criteria and their descriptions (Nagar and Raj, 2012) EVALUATION CRITERIA DESCRIPTION

Flexibility (C1) This attribute incorporates the frequently changing technology and demands of the market in today’s world

Productivity (C2) The productivity is another attribute for finding out the effectiveness of a production system.

Inventory (C3) The list of movable items required for the finished good to be out.

Operating cost (C4) The operating cost directly impacts on the performance of a manufacturing system. It is the money spent in operating the plant. It varies with the change in the volume to be produced.

Responsiveness (C5) It is the ability of the manufacturing system to respond to the predictable and unpredictable changes by utilising its existing resources.

Reconfigurability (C6) The ability to adopt changes in the hardware and software of a manufacturing system to machine a new part.

Based on the above-stated attributes for comparing the four manufacturing systems, i.e. CNCs, CIMS, FMS and HFMS. The best manufacturing system is identified, based on the experts’ opinion, according to Indian industrial environment.

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Table 6: Alternative manufacturing systems ALTERNATIVE DESCRIPTION REFERENCE

CNC machines CNC machines are being built with features which enable complete or almost complete manufacture of components in a single set-up, thus

providing flexibility in operations, quality, products, process and machines.

Nagar and Raj (2012)

Flexible manufacturing

system

A set of computer numerically controlled (CNC) machine tools and supporting workstations that are connected by an automated material

handling system and are controlled by a central computer’.

Yılmaz and Erol (2015)

CIMS A CIM system is an approach of employing computers to manage and control the entire manufacturing process. Through the information and

process integration of computers by using its subsystems [e.g., computer-aided design (CAD), computer-aided engineering (CAE),

computer-aided manufacturing (CAM), computer-aided process planning (CAPP), computer-aided quality assurance (CAQ), production

planning and control (PPC), enterprise resource planning (ERP), etc.

Tao et al., (2015)

Humanized flexible manufacturing

system

HFMS consists of the different components like machine tools and related equipment’s, human element for material handling and

computer control system.

Nagar and Raj (2013)

Best Manufacturing

System

Best Manufacturing

System

Reconfigurabil

ity

Reconfigurabil

ityOperating CostOperating Cost ResponsivenessResponsivenessInventoryInventoryProductivityProductivityFlexibilityFlexibility

CNC SystemCNC System FMSFMS CIMSCIMS HFMSHFMS

Figure 3. Analytical Hierarchy Structure of Manufacturing Systems Swara Results As mentioned before in introduction, SWARA method is applied in this part for evaluating and weighting the model criteria. The results of SWARA method are shown in Tables 7.

Table 7: Final results of SWARA method in weighting criteria Criteri

on Comparative importance of

average value S j Coefficient K j= S j +1 Recalculated weight

Weight

∑

(C4) - 1 1 0.2348

(C2) 0.16 1.16 0.8620 0.2024

(C5) 0.24 1.24 0.6952 0.1632

(C1) 0.11 1.11 0.6263 0.1470

(C3) 0.09 1.09 0.5746 0.1349

(C6) 0.15 1.15 0.4996 0.1173

*Operating cost was selected as the most important criterion in stability criteria.

COPRAS-G Results In this section, COPRAS-G method is used to evaluate and select alternatives after determining all weights of each criterion and sub-criterion through SWARA method. Results of COPRAS-G method are shown in Tables 8-10.

Table 8: Initial decision making matrix with the criteria values described in intervals ⊗x1 ⊗x2 ⊗x3 ⊗x4 ⊗x5 ⊗x6

Opt Min Max Max Max Max Max

qi 0.2348 0.2024 0.1632 0.1470 0.1349 0.1173

Alternatives 1 , 1 2 , 2 3 , 3 4 , 4 5 , 5 6 , 6

CNC 8 10 5 8 5 8 5 8 5 8 8 10

FMS 8 10 8 10 8 10 8 10 5 8 5 8

CIMS 8 10 5 8 5 8 5 8 3 5 1 3

HFMS 1 3 8 10 8 10 8 10 1 3 3 5

272 , G.Jamali et.al / Teknologi Tanaman /Vol (12), Supp (2) 2015

Besides, Table 8 indicates initial decision making matrix, with the criterion values. For the weight qi of criteria, we used weights in Tables 7. As mentioned before, the aim of using SWARA is to determine importance weight of criteria that will be employed in COPRAS method.

Table 9: Normalized weighted decision making matrix ⊗x1 ⊗x2 ⊗x3 ⊗x4 ⊗x5 ⊗x6

Opt Min Max Max Max Max Max

Alternatives 1 , 1 2 , 2 3 , 3 4 , 4 5 , 5 6 , 6

CNC 0.0809 0.0647

0.0522 0.0326

0.0421 0.0263

0.0379 0.0237

0.0568 0.0355

0.0558 0.0446

FMS 0.0809 0.0647

0.0652 0.0522

0.0526 0.0421

0.0474 0.0379

0.0568 0.0355

0.0446 0.0279

CIMS 0.0809 0.0647

0.0522 0.0326

0.0421 0.0263

0.0379 0.0237

0.0355 0.0213

0.0167 0.0056

HFMS 0.0080 0.0242

0.0652 0.0522

0.0526 0.0421

0.0474 0.0379

0.0071 0.0213

0.0279 0.0167

The initial decision making matrix has been normalized first, as discussed in previous section. The weighted normalized decision making matrix is presented in Table 10. According to the results of Table 10 which shows evaluation of utility degree, HFMS > FMS > CNC > CIMS.

Table 10: Evaluation of utility degree Alternatives P j R j Q j N j Rank

CNC 0.4075 0.0728 0.4386 85.78% 3

FMS 0.4622 0.0728 0.4933 96.48% 2

CIMS 0.2939 0.0728 0.352 63.56% 4

HFMS 0.3704 0.0161 0.5113 100% 1

According to the last column of Table 10, HFMS is defined as the best alternative for Automotive Industry.

Conclusions The main purpose of this study is to analyze and evaluate dimensions of AMS. It is based on this perspective. In order to make a good decision and better selection, two MCDM methods are applied. 5 experts participate in this research. Experts have three important roles. At first, they establish the model of research at a conference meeting, then they involve in solving SWARA method for prioritizing and weighting criteria of research model, and finally, involve in solving COPRAS-G for evaluating and ranking alternatives. The criteria of this research are flexibility, productivity, inventory, operation cost, responsibility and reconfiguration. Final results of SWARA method are shown in Tables 7 and Operating cost is the most important criteria in the research model. Four AMS are selected for this research that are CNC, FMS, CIMS and HFMS. Final results of COPRAS show that HFMS are the best choice for automotive industry in Iran.To meet the challenges imposed by today’s dynamic market conditions, it is the right time for the manufacturing companies to transit from traditional manufacturing system to advance manufacturing system such as CNC, FMS and CIM systems. CNC, FMS and CIM are very well-known terms in the manufacturing world. Developed countries have adopted and used all these manufacturing systems in their industries. Developing countries have used CNC technology in their plants but are reluctant to opt for the next phase of AMS, i.e. FMS or CIM due to their high cost and heavy risk associated with them. Hence, a new manufacturing system, i.e. humanized FMS suitable to Iranian industrial and social environment has been proposed in this paper.Merely proposing this new manufacturing system, i.e. HFMS, does not solve the problems of industries. Hence, a lot of work is required in this area in future, such as: case studies regarding comparison of the performance of AGVs with human operated material handling systems, case studies regarding the use of human element in job setting and tool setting during product/operations changes, case studies involving human element in solving loading and scheduling problems of AMS, the other methods for ranking and evaluation of criteria and alternatives.

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References 1. Aghdaie, M. H., Hashemkhani Zolfani, S. & Zavadskas, E. K. (2013). Decision Making in Machine Tool Selection: An Intergrated Approach with SWARA and COPRAS-G Methods. Inzinerine Ekonomika-Engineering Economics, 24(1), 5-17. 2. Aghdaie, M. H., Hashemkhani Zolfani, S., and Zavadskas, E. K. (2014). Sales Branches Performance Evaluation: a Multiple Attribute Decision Making Approach. 8th International Scientific Conference “Business and Management 2014”, 1-7. 3. Azzone, G., & Bertele, U. (1989). Measuring the economic effectiveness of flexible automation: a new approach. The International Journal of Production Research, 27(5), 735–746. 4. Barad, M., and Sipper, D., (1988). Flexibility in manufacturing systems: definitions and petri net modelling. International journal of production research, 26, 237-248. 5. Baruwa, O. T., and Piera, M. A. (2015). Identifying FMS repetitive patterns for efficient search-based scheduling algorithm: A colored Petri net approach. Journal of Manufacturing Systems, 35, 120–135. 6. Browne, J., Dubois, D., Rathmill, K., Sethi, S.P. and Stecke, K.E. (1984). Classification of flexible manufacturing systems. FMS Magazine, 2(2), 114–117. 7. Candan, G., and Yazgan, H. R. (2015). Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem. International Journal of Production Research, 53(3), 897–915. 8. Carida, V. F., Morandin Jr, O., and Tuma, C. C. M. (2015). Approaches of fuzzy systems applied to an AGV dispatching system in a FMS. Int J Adv Manuf Technol, 1-11. 9. Dubey, R., and Ali S. S. (2014). Identification of Flexible Manufacturing System Dimensions and Their Interrelationship Using Total Interpretive Structural Modelling and Fuzzy MICMAC Analysis. Global Journal of Flexible Systems Management, 1-13. 10. Erdin, M. E., and Atmaca, A. (2015). Implementation of an overall design of a Flexible manufacturing system. Procedia Technology, 19, 185–192. 11. Haghnazar Kouchaksaraei, R., Hashemkhani Zolfani, S., and Golabchi, M. (2015). Glasshouse Locating based on SWARA-COPRAS Approach. International Journal of Strategic Property Management, 19(2), 111-122. 12. Hashemkhani Zolfani, S. and Bahrami, M. (2014). Investment Prioritizing in High Tech Industries based on SWARA-COPRAS Approach. Technological and economic development of Economy, 20(3), 534-553. 13. Hashemkhani Zolfani, S., and Zavadskas, E. K. (2013). Sustainable Development of Rural Areas’ Building Structures Based on Local Climate. Procedia Engineering, 57, 1295-1301. 14. Hashemkhani Zolfani, S., Maknoon, R., and Zavadskas, E. K. (2015). Multiple Nash Equilibriums and Evaluation of Strategies. New Application of MCDM Methods. Journal of Business Economics and Management, 16(2), 290-306. 15. Hashemkhani Zolfani, S., Rezaeiniya, N., Aghdaie, M. H., and Zavadskas, E. K. (2012). Quality Control Manager Selection based on AHP- COPRAS-G Methods: A case in IRAN. Ekonomska istrazivanja, 25(1), 88-104. 16. Hashemkhani Zolfania, S., Zavadskas, E. K., and Turskis, Z. (2013). Design of Products with both International and Local Perspectives based on Yin-Yang Balance Theory and Swara Method. Ekonomska Istrazivanja-Economic Research, 26(2), 153-166. 17. Jain, V., and Raj, T. (2013). Ranking of Flexibility in Flexible Manufacturing System by Using a Combined Multiple Attribute Decision Making Method. Global Journal of Flexible Systems Management, 1-17. 18. Jamali, Gh. (2014). Forecast market share and the possibility of retention and movement of bank customers in Bushehr: Compare the analysis of the MARKOF chain and SWARA. Journal of Operations Research and Applications, 11(14), 75-87. 19. Karande, P., and Chakraborty, Sh. (2013). Evaluation and selection of flexible manufacturing systems using MACBETH method. Int. J. Services and Operations Management, 16(1), 123-144. 20. Karsak, E.E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. Int. J. Prod. Res., 40(13), 3167-3181. 21. Kashfi, M. A., and Javadi, M. (2015). A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making. Prod. Eng. Res. Devel, 9, 237–246. 22. Kersuliene, V., and Turskis, Z. (2011). Integrated Fuzzy Multiple Criteria Decision Making Model for Architect Selection. Technological and Economic Development of Economy, 17(4), 645-666. 23. Kersuliene, V., Zavadskas, E. K., and Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step_wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. 24. Lin, J. T., Chiu, Ch-Ch., Chang, Y-H., and Chen, H-M. (2015). A Hybrid Genetic Algorithm for Simultaneous Scheduling of Machines and AGVs in FMS. Industrial Engineering, Management Science and Applications 2015, Lecture Notes in Electrical Engineering, 277-286. 25. Lioua, J. J. H. Tamosaitiene, J., Zavadskas, E. K. and Tzeng, G-H. (2015). New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. International Journal of Production Research, 1-21. 26. Nagar, B. & Raj, T. (2012). Analysis of critical success factors for implementation of humanised flexible manufacturing system in industries. Int. J. Logistics Economics and Globalisation, 4(4), 309-329. 27. Nagar, B., and Raj, T. (2012). An AHP-based approach for the selection of HFMS: an Indian perspective. Int. J. Operational Research, 13(3), 338-358. 28. Nagar, B., and Raj, T. (2013). Digraph and matrix evaluation for shifting to humanised flexible manufacturing system. Int. J. Logistics Economics and Globalisation, 5(2), 149-165. 29. Novas, J. M., and Henning, G. P. (2014). Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming. Expert Systems with Applications, 41, 2286–2299. 30. Park, C. S., & Son, Y. K. (1988). An economic evaluation model for advanced manufacturing systems. The Engineering Economist, 34(1), 1–26. 31. Rey, G. Z., Bonte, T., Prabhu, V., and Trentesaux, D. (2014). Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach. Simulation Modelling Practice and Theory, 46, 53–75. 32. Rey, G. Z., Bonte, T., Prabhu, V., and Trentesaux, D. (2014). Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach. Simulation Modelling Practice and Theory, 46, 53-75. 33. Ruzgys, A., Volvaciovas, R., Ignatavicius, C., and Turskis, Z. (2015). Integrated Evaluation of External Wall Insulation in Residential Buildings using SWARA-TODIM MCDM Method. Journal of civil engineering and management, 20(1), 103-110. 34. Sanches, D. S., Rocha, J. d. S., Castoldi, M. F., Morandin Jr, O., and Kato, E. R. R. (2015). An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs. J Control Autom Electr Syst, 1-10. 35. Sethi, A. K., & Sethi, S. P. (1990). Flexibility in manufacturing: A survey. International Journal of Flexible Manufacturing Systems, 2(4), 289–328.

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36. Shrivastava, A. (2015). Scheduling of Flexible Manufacturing Systems using Fuzzy Logic: A Review. International Conference of Advance Research and Innovation, 289-295. 37. Singholi, A., Ali, M., and Sharma, Ch. (2013). Evaluating the effect of machine and routing flexibility on flexible manufacturing system performance. Int. J. Services and Operations Management, 16(2), 240-261. 38. Skeete, A., Mobin, M., and Salmon, Ch. (2015). Aviation Technical Publication Content Management System Selection Using Integrated Fuzzy-Grey MCDM Method. Proceedings of the 2015 Industrial and Systems Engineering Research Conference, 1-10. 39. Son, Y. K., & Park, C. S. (1990). Quantifying opportunity costs associated with adding manufacturing flexibility. The International Journal of Production Research, 28(6), 1183–1194. 40. Stanujkic, D., Karabasevic, D., and Zavadskas, E. K. (2015). A Framework for the Selection of a Packaging Design Based on the SWARA Method. Inzinerine Ekonomika-Engineering Economics, 26(2), 181-187. 41. Tao, F., Cheng, Y. Zhang, L. and Nee, A. Y. C. (2015). Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf, 1-16. 42. Vafaeipour, M., Hashemkhani Zolfani, S., Morshed Varzandeh, M. H., Derakhti, A., and Keshavarz Eshkalag, M. (2014). Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach. Energy Conversion and Management, 86, 653-663. 43. Wahab, M. I. M. and Osman, M. F. S. (2013). A fundamental flexibility measure: Machine flexibility. 1-5. 44. Xia, F., Wei, H., and Yang, L. (2015). Improved COPRAS Method and Application in Material Selection Problem. Applied Mechanics and Materials, 707, 505-508. 45. Yamamoto, H., Yokoi, K., and Yamada, T. (2015). Production effects by form changes of autonomous decentralized FMSs with mind. Journal of Robotics, Networks and Artificial Life, 1(4), 295-298. 46. Yılmaz, E., and Erol, R. (2015). A mathematical programming model for reconfiguration of flexible manufacturing cells. Engineering Optimization, 47(2), 184-203. 47. Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamosaitiene, J. (2009). Multi-Attribute Decision-Making Model by Applying Grey Numbers. Informatica, 20(2), 305–320.

Gholamreza Jamali, Department of Industrial Management, Persian Gulf University, Bushehr, Iran Corresponding Author Email: gh_jamali@yahoo.com KH.Farrokhnejad, Research Studies Center, Persian Gulf University, Bushehr, Iran Masoumeh Mohammadi, MSc. Student, Department of Industrial Management, Persian Gulf University, Bushehr, Iran