CBR Approach in BOT Project Risk Assessment

Ning Liu School of Management

Dalian University of Technology Dalian, China

dllgdxglxy@163.com

Dashuang Dai School of Management

Dalian University of Technology Dalian, China

daids@dlut.edu.cn

Haixi Wu School of Management

Dalian University of Technology Dalian, China

Whxt1976@163.com

Abstract—This paper uses case-based reasoning (CBR) approach to establish estimation method of Build-Operate-Transfer (BOT) project risk, through a CBR assessment system that helps stakeholder to master the specific features of the BOT project under the present conditions. Affected by numerous factors related both to the specific features of the project and dynamically changed situations, BOT projects risk assessment problems are highly unstructured. Risk management aims to anticipate the future performances of the project and the economic parameter range of the project. Through the CBR system, similar cases can be retrieved to assess the possible influence of BOT project risk degree. An example of garbage burning power plant in china is explained and evaluated to demonstrate the feasibility of the method.

Keywords- CBR; BOT project; garbage burning power plant; risk assessment

I. INTRODUCTION The large demand of infrastructure and energy projects

provide a broad stage for project financing strategies in China. BOT-type schemes have been used in power, water supply, transport, telecommunication and process plant sectors. Nowadays, more local governments are positioning to expand their investment in infrastructure construction through BOT model of project financing.

The BOT project stakeholders should give necessary importance to risk management concept which covers risk identification, analysis and response development stages. The reason to take risk assessment concept into the BOT project is which risk structure is subject to more risk and uncertainty than many other projects due to requirement of multitude of stakeholders with different interests, the coordination of a wide range of interrelated activities and vulnerability of construction projects to political, economic, social and environmental conditions. This situation leads to the definition of new concepts in BOT project management (PM) field such as risk information modeling, risk register data base systems and decision support systems which are designed to assist the stakeholders during the decision making process.

There are many risks associated with BOT projects such as high financial risk, market risk, cost overrun risk, political risk, and operation risk [1]. The high-risk exposure associated with BOT projects implies that the decision-makers or negotiators of both the BOT concession company and the government must pay special attention to analyzing and managing risks [2].

Intuition, expert skills and judgment will always influence decision making, but a set of tools is needed which enable PM techniques to be put into practice in field of BOT project. Previous studies have shown that methods such as utility theory, statistics, team theory, and mathematical programming can be employed to measure the risks of BOT projects. Zayed and Chang[3] used the concept of utility theory to derive the weighted expected value as a risk index of BOT projects, while ignoring the decision-makers’ preference. David [4] developed the expected production cost for investor and the host utility using the statistics approach to obtain risk value for BOT projects. Bell [5] showed that the maximum value of expected utility can reveal the characteristics of high return and high risk. Feng and Kang [6] employed the MAU theory to evaluate risk for BOT concession contracts. Their studies examined risk preferences of the negotiators and determined the primary and secondary risks associated with BOT projects. However, no effect has yet been made to assess risks with interactive utility among negotiators. No clear rules can be found in estimating BOT project risk. Thus, decisions are commonly made based upon intuition and past experience.

II. CBR CBR is a sub branch of artificial intelligence. It solves new

problems by matching against similar problems that have been encountered and resolved in the past. CBR is a general paradigm for problem solving based on the recall and reuse of specific experiences.[7] Also CBR is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved. Because one of the main aspects underlying CBR theory is learning from experience, it requires a well worked out set of methods in order to extract relevant knowledge from the experience, integrate a case into an existing knowledge structure, and index the case for later matching with similar cases. Today, over 30 years of research and development, CBR has become a mature technology for several application areas, such as diagnosis, decision support, design, or planning. [8]

It is important to ensure that the right cases can be recalled at the right times. CBR has two aspects. One is the vocabulary problem that requires appropriate labels be assigned to the case so that it can be easily referenced in the case library during retrieval. The other problem is that of organizing the cases so that searching through the case library can be efficient and accurate. The case organization aspect will be addressed later.

This research is supported by NSFC of china under Grant 70572097

978-1-4244-4639-1/09/$25.00 ©2009 IEEE

III. MAIN PROCEDURES The biggest issue in CBR is the retrieval of appropriate

cases. In this system, BOT project cases are represented by sets of attributes derived from a preliminary survey of several cases in service, focusing, respectively, on two reasoning sub goals: (1) Profit status; and (2) Risk influence degree.

The case adaptation module takes the analysis and derives an alternative markup level based on the criterion of maximized utility value by taking into consideration the decision-maker’s risk preference as judged from need for return and risk in the type of BOT project. The outcome of the new bid case, whether a failure or success, needs to be recorded into the case base so as to provide a lesson for future situations. The framework for estimation method of BOT project risk is depicted in Fig.1.

Caseinformation

Casepresentation

Characteristicsof Case risk

Casematch

information of Risk Management Program

cases of maximum likelihood

Case base OfRisk characteristic

Case base OfRisk management

informationgather

Casegather

Caseadjust

BOT project risk management planning

CaseMaintenance

Caseinformation

Casepresentation

Characteristicsof Case risk

Casematch

information of Risk Management Program

cases of maximum likelihood

Case base OfRisk characteristic

Case base OfRisk management

informationgather

Casegather

Caseadjust

BOT project risk management planning

CaseMaintenance

Figure 1. Estimation method of BOT project risk

A. Framework for case base Past BOT project cases are stored in the case base. Factors

that the decision maker considers to be significant determinants of the project characteristic markup are built into the system as the domain knowledge. The case base coupled with the domain knowledge constitutes the knowledge base of the system. Too many indices, however, can impair the efficiency of the case based reasoning. Thus the index will comprise the key determining factors, respectively.

For BOT project, different sub-case bases should be established, because great differences exist among the projects with different type of project. Characteristic attributes and solution attributes are separately defined in each sub-case base.

The selection of case risk characteristics should according to the analysis and statistical data of history engineering, and the experience of experts determined. Tasks and domains must be analyzed to find the functionally relevant labels or descriptors. These labels or descriptors are referred to as the indexing vocabulary or case attribute. Any case vocabulary

must be able to represent the specific and relevant features of a case [9].

Take garbage burning power plant project for example, TABLE I shows the key characteristic attributes with respect to different importance weightings which can be calculated by the analytic hierarchy process (AHP) from experience. The solution attributes (SA) are defined as Profit status (PS) and of Risk influence degree (RD).

TABLE I. STRUCTURE HIERARCHY AND WEIGHT OF THE ATTRIBUTES

No. Factors Unit Weights

1 Aggregate investment (AI) RMB 0.05

2 Concession period (CP) Year 0.15

3 Garbage calorific value (GV) kJ/ kg 0.1

4 Supply rate (SR) % 0.15

5 On-grid price (OP) Yuan/kWh 0.25

6 Garbage disposal subsidy (GS) Yuan/ton 0.25

7 Garbage disposal ability (GA) ton/day 0.05

The determining factors should serve as good potential candidates for the case indexing vocabulary. The importance weightings shown in the table have been normalized to sum up to 1.

B. New case description The new case description process is to extract the

characteristic attributes, which is similar to the previous procedure. This paper introduce fuzzy mathematics into the similarity comparison between the old cases and the new case, give a fuzzy description of new case and determine the membership degree of the new case in each characteristic attribute to set up fuzzy set of new case.

The fuzzy mathematics description is as follows:

( )fVAUS ,,,= , DYA ∪= , ϕ=DY ∩ (1)

S : Knowledge system

A : The limited property set;

V : The characteristic attributes collection;

D : The solution attributes;

{ }nuuuU ,,, 21= : The most basic unit slot corresponding attributes of the case base.

{ }myyyY ,,, 21= : The characteristic attributes set, which can be expressed as:

StandardY , obtain { }mfffY ,,,~21=

( )mjf ,,2,1j = : Quantitative data standard by Eq (2):

⎪⎪⎪⎪

⎩

⎪⎪⎪⎪

⎨

⎧

∈−+

∈−−

∈−

−

=

∗∗

∗

3

2minmax

max

1minmax

min

,

,

,

Kjyyy

y

Kjyyyy

Kjyy

yy

f

jjj

j

j

j

j (2)

1K : Positive attributes aggregation

1K is positive attribute aggregation;

2K is negative attribute aggregation;

3K is approach attribute approach;

∗jy : Optimal numerical value of attribute j

( )jui fμ : The membership degree of case iu

corresponding to the characteristic attribute jf ,

Then jf vector set of case iu can be expressed as:

{ }muiuiuiui fffV μμμ ,, 21= (3)

The jf vector set of new case T can be expressed as:

{ }mTTTT fffV μμμ ,, 21= (4)

Any property fuzzy subset iA~ handing after the process above is deposited to database with PS and RD .

Pending case through the same process translates into fuzzy sub set R~ [10].

Based on studying the relationship between inclusion degree and close degree, this paper proposes an improved fuzzy clearness degree case-matching method. The calculation formula is as follows (Eq.5) [11].

[ ]( )=BAcs ~,~

( ) ( )( )

( ) ( )( ) ×

⎪⎩

⎪⎨⎧

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ∧∨

∧∑

=

n

j j

jj

j

jjj uB

uBuAuA

uBuAw

1c

cc

~~~

~~~

( ) ( )( )

( ) ( )( )

21

1c

cc

~~~

~~~

⎪⎭

⎪⎬⎫

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ∧∨

∧∑

=

n

j j

jj

j

jjj uA

uAuBuB

uAuBw (5)

jw : The effect weight of the characteristic attributes j

This formula takes into the different weights of attributes, applying to multi attribute model such as BOT project risk

assessment. Compared with the conventional nearest neighbor approach, the adaptation method based on fuzzy proximity can improve the retrieval precision, and the new way for case adaptation will improve the quality of solutions.

Close degree [ ]( )RAcs ~,~between iA~ and R~ can be

calculated by Eq (5). Threshold value (ξ ) of close degree is set as 0.85 from experience. All cases according with

[ ]( ) ξ≥RAcs ~,~ need further adjustment.

C. Case adaptation The case adaptation methods are various, and can be

divided into single adaptation and combined adaptation. This paper prefers the combined adaptation, which is to generate a new solution from solutions of suitable cases, with the purpose of getting a more accurate solution by making good use of information of each case. Based on statistical analysis to adjust the way, the case will be few to match the most similar case for statistical analysis.

Solution attributes space can be defined as: (Eq.6)

nd Ω=Ω ， [ ] ( ){ }dacsya Yn ,∈=Ω (6)

d : One of the solutions attributes；

nΩ : Redundant spaces of solution attribute d .

Assume bYYYY ∪∪∪ 10= , φ=bYYY ∩∩∩ 10

0Y : A redundant attribute set for all solution attributes.

It can easily determine bYYY ,,, 10 the corresponding solution attributes set based on the previous step [10].

Difference attributes set ( M ) is the foundation to implement case adaptation as follows (Eq.7) :

{ }tetee YaaaYaM ∈≠∈= , (7)

eY : The stored cases’ characteristic attributes set;

tY : The new case’s characteristic attributes set;

ea : Case e attribute;

ta : Case t attribute;

To each ( )bjYj ,,1,0= , we can match the most similar case from the retrieved cases. This case can be use to adapt the corresponding solution attribute d .

In accordance with standard [ ]( ) ξ≥RAcs ~,~, we can find

the similar cases kAAA ,,, 21 and the solution attributes

kLLL ,,, 21 . The result of BOT project risk assessment can be expressed as:

{ }kk PLPLPL ,;,;, 22111

[ ]( )RAcsaP i

k

iijj

~,~1∑

=

⋅= (7)

ija : The fuzzy numbers of degree j at Case i

D. Case maintenance Case system case should be made to get sustained accuracy

and efficiency. Maintenance method can be expressed as:

• The representative case matching successfully is deposited in the corresponding case base;

• The case adopting hardly ever should be deleted;

• Finding it is difficult to match a similar case, the case base must be supplemented by typical cases.

IV. APPLICATION

A. Data The data of the case base in this paper came from the actual

garbage burning power plant that were built between 2002 and 2008 in China. 40 projects accord with criterion can be partly seen in Table II.

TABLE II. CASE BASE OF PROJECT

No. CA SA

AI CP GV SR OP GS GA PS RD

1 2.70 25 7300 90% 0.550 25 500 losses very high 2 3.20 30 6200 100% 0.737 120 600 profit low 3 4.80 25 7100 80% 0.666 198 850 profit very low 4 3.50 25 6500 100% 0.550 120 650 breakeven normal 5 2.87 22 6900 100% 0.689 145 550 breakeven normal 6 4.60 20 6800 70% 0.550 105 800 breakeven normal 7 2.92 30 5800 90% 0.546 55 550 losses high 8 4.20 25 5700 50% 0.574 50 750 heavy losses very high 9 3.48 25 6900 100% 0.550 45 650 breakeven low … … … … … … … … … …

The characteristic attributes of a new case can also be described using the previous method, which can be seen in Table III.

TABLE III. A NEW CASE’S CHARACTERISTIC ATTRIBUTE

No. CA

AI CP GV SR OP GS GA 1 3.90 25 7200 100% 0.550 100 700

The close degrees between the new case and the stored cases were calculated by Eq.(1). Assume the threshold value of similarity degree (ξ ) is 0.85, and then the cases which meet the criterion should be retrieved for case adaptation.

The retrieved cases and the calculated result are illustrated in Table IV.

TABLE IV. THE RESULT OF THE CBR

No.

CA SA SD

AI CP GV SR OP GS GA PS RD

1 3.70 25 7200 100% 0.550 110 700 profit low 0.9541

2 4.05 25 7200 100% 0.575 100 750 Breakeven low 0.9212

3 3.50 25 6800 100% 0.550 120 650 Breakeven normal0.8975

T 3.90 25 7200 100% 0.550 100 700 Breakeven normal1.0000

PST { }67.0,;33.0, breakevenprofit means that probability of profit is 0.33 and probability of breakeven is 0.67 .

RDT { }33.0,;67.0, normallow means that probability of low is 0.67 and probability of normal is 0.33 by Eq (1) .

V. CONCLUSION BOT project is a “live” template. The government must

ensure that projects be developed efficiently, providing good quality public service. BOT type schemes must thus achieve win-win outcomes for both the private and the public interests.

Our numerical example shows that the BOT project risk assessment model developed can accomplish risk assessment more efficiently and simpler than other traditional algorithm. This estimation method can be also for the other project if we change some index. It will be more efficiently for the stakeholder to estimate the project risk.

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and BOT plant investors,”IEEE Transactions on Energy Conversion,pp. 359-366,Jun l996.

[5] Bell DE, “Risk,return,and utility,”Management Science,pp.23-30,Jan 1995. [6] Feng CM and Kang CC, “Risk identication and measurement of BOT

projects,”Journal of the Eastern Asia Society for Transportation Studies ,pp.331-350,Apr 1999.

[7] De Silva, Garza AG and Maher ML, “An evolutionary approach to case adaptation, case-based reasoning research and applications,” Proceedings of the Third International Conference on Case-Based Reasoning, ICCBR-99, Munich,Jul 1999.

[8] J.Toussaint and K.Cheng, “Web-based CBR(case-based reasoning) as a tool with the application to tooling selection,” Springer-Verlag London Limited 2005 , pp. 24-34, Jul 2005.

[9] D.K.H.Chua, D.Z.Li and W.T.Chan, “case-based reasoning approach in bid decision making,” journal of construction engineering and management, pp.35-45,feb 2001.

[10] Wu Yunna and Huang Zhijun, “Application of a case-based reasoning method in estimating the power grid project cost,” international conference on engineering management and service sciences (EMS2008),dalian,Oct 2008.

[11] C. ZH. Wang, Y. J. Guo, Zh. J. Yu, W. Chen, “CBR-Based Control System of Credit Ris,” Journal of Northeastern University (Natural Science). Shenyang, Vol.28, No.3, pp. 449-453, Mar. 2007.