Study of Supply Chain by VIKOR Method

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1 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: Study of Supply Chain by VIKOR Method Akhil Mohan Mishra 1, Amit Ahlawat 2, Rahul 3, Jitendra singh Baghel 4 1 M.tech in Manufacturing and automation from Maharshi Dayanand University, Haryana 2 Assistant Professor in C.B.S. roup of Institution, Jhajjar Haryana. 3 Professor in C.B.S. roup of Institution, Jhajjar Haryana. 4 Senior executive in Honda cars India Ltd. ( Purchase Deptt.) ABSTRACT In today s competitive global markets, selection of a potential supplier plays an important role to cut production costs as well as material costs of the company. This leads to successful survival and sustainability in a competitive marketplace. Therefore, evaluation and selection of an appropriate supplier has become an important part of supply chain management. In this paper, we have revised the VIKOR model to consider both the choice less and discontent utilities. In addition, the levels of regrets are not normalized in order to avoid the possible problem of the preference ranking of alternatives. So to obtains the preference ranking. regret is only matter with the difference between alternatives and the best value of each criterion, it is unnecessary to normalize the levels of regret involvement of the subjectivity and uncertainty situation in the multi-criteria decision making process. Moreover, decision makers are frequently faced with confusions, and difficulties while dealing with this kind of decision making process. To overcome this vagueness, ambiguity and subjectivity of the human judgment process, UZZY SET THEORY has been introduced which could give optimum solution. Keywords: Supplier selection, VIKOR model, UZZY SET THEORY, MCDM. 1. INTRODUCTION In today s world supply chain management is becoming more and more important for companies. In this process we consider the flow of goods and services. It include the movement and storage of raw materials, work-inprocess inventory, and finished goods from point of origin to point of consumption. We are facing problems like selection of potential, production costs, material costs of the company. Therefore, evaluation and selection of an appropriate alternative has become an important part of supply chain management. This process is multiple criteria decision making (MCDM) which concerns the problem of multiple conflicting criteria, so we use VIKOR [1-3]method for solving supply chain problem Suppliers Selection (An Overview) Supplier selection is an important decision-making process in supply chain management. Different suppliers have varied pros and cons associated with them. Therefore, selecting an appropriate supplier is always a difficult task. Supplier section has a major impact on proper functioning of supply chain as well as product quality. Selection of right supplier improves the efficiency of supply chain and significantly increases corporate competitiveness. Organizations must be very cautious not only about price and quality of raw material but also about the structure of the organization, production capabilities, reliability, company policy etc. or some cases, it is not only enough to look at supplier conditions but also supplier s supplier reliability and capability. or the case of Just in Time (JIT) manufacturing, supplier selection is of utmost importance. Today at an average manufacturer spends approximately half its revenue to purchase goods and services, thus making a company s success reliant on their interactions with suppliers. The role of procurement managers within companies has become particularly important. Supplier selection involves the congregation of decisions made by different organizational levels in the company. Each level or each department may have their own priorities based on their ease of manufacturing. Taking all these into study, one cannot have an optimal solution. So, in selecting an appropriate supplier, one has to consider all these requirements and should take a compromising decision. With much of company s money being spent and increasing dependency on the outsourcing of many critical and complex parts, the role of buyer is not only critical but also challenging. Buyers must define and calculate what will be the best value means for the buying organization, and undertake procurement actions accordingly. To identify the best value, the procurement manager must have a common meeting with technical, operations and legal experts within the company, and should be a professional negotiator and director across many internal and external parties. Supplier selection is 328

2 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: the process by which the buyer identifies, estimates, and deals with suppliers. The challenges mentioned make supplier selection a rich topic for industrial operations and management disciplines. To cope with the growing competition it isn t enough only to select from the existing or known suppliers but the management should be able to identify new suppliers. New supplier identification is so important that it might a novel production technology or may have structural advantage or low labor cost which ultimately impact the cost of the product and may be able to supply it for cheaper than any other or may be able to deliver with lesser lead time that might allow to maintain minimum inventory which reduces expenses for maintenance as well as money will be put to best use. Again there involves a key factor for selection of suppler i.e. risk associated with them. Supplier risk is more important than any because it directly affects the buyer in all aspects of production involving that part or operation that has to be done only after the part is assembled. These situations may lead to complete halt of the company incurring huge loss in the form of manufacturing holiday, idling of labor, loss of customer faith etc. Sometimes it can be even worse and might lead to lockup of the factory. Therefore, supplier selection, evaluation and monitoring are crucial for an industry to survive in long term. Ranking of suppliers becomes complex when suppliers must be evaluated across multiple dimensions/ evaluation indices. or example, if the buyer wishes to evaluate supplier s bids on the extents of price and leadtime, the buyer must build a trade-off between these two dimensions to determine whether it favors, say, a bid with a high price and less lead time to a bid with a low price and higher lead time. The real challenge of supplier evaluation lies in constructing this tradeoff in a way that perfectly reflects the buyer s preferences [4] Suppliers Selection: Prior State of Art Supplier selection is the process by which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm s financial resources. In return, firms expect significant benefits from contracting with suppliers offering high value [5]. Supplier selection is the process by which the buyer identifies, evaluates, and contracts with suppliers. The challenges mentioned above make supplier selection a fertile topic for operations and management science disciplines. There is also a growing audience for such research, as the importance of fostering talent by employing buyers with analytical expertise, general management backgrounds, and deep knowledge in a particular purchasing category becomes widespread [6].The general structure of risk management methods in a complex network environment. This study helped to understand the risk of other companies thus facilitate in choosing the right supplier [7].A hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory to deal with the supplier selection problems in the supply chain system. According to the concept of the TOPSIS, a closeness coefficient was defined to determine the ranking order of all suppliers by calculating the distances to the both fuzzy positive-ideal solution (PIS) and fuzzy negativeideal solution (NIS) simultaneously. inally, an example was shown to highlight the procedure of the proposed method [8].Some critical decision-making criteria along with the risk factors for development of an effective structure for selection of appropriate supplier. uzzy extended Analytic Hierarchy Process (AHP) was used to handle the different criteria of supplier s selection like cost, service, quality, and risk profile of the supplier [9].Analytical hierarchy process to evaluate and rank potential suppliers. Ranking was done among the potential foreign customers based on several criteria of supply chain reliability including potential risk of the supplier [10].Concepts based on supplier s attributes, performances and supply chain characteristics which also depended on the supplier s specific environment in which they were supposed to operate, for the assessment and classification of suppliers [11] Problem Statement In the present context, supply chain evaluation is seemed to be a Multi-Criteria Decision Making (MCDM) problem under complexity and vagueness. To tackle the problem, this study explores fuzzy evaluation of the decision criteria and constructs the framework to select the supplier involved with organizations and their supply chains. In this method, the selection related parameters has been known (cost, duration and material handling) and the vagueness and subjectivity have been handled with linguistic terms parameterized by triangular fuzzy numbers. A fuzzy-based risk assessment module has been developed in this reporting. Supplier s selection is one of the crucial components in supply chain. Appropriate suppliers selection decision-making can mitigate optimality. The objective of supplier selection is to reduce purchasing risk, maximize overall value to the purchaser and build a long term, reliable relationship between buyers and suppliers. Many methods have been proposed and used for supplier evaluation and selection; most of them try to rank the suppliers from the best to the worst and to choose the appropriate supplier(s). Supplier evaluation and selection is a complex and typical multi criteria decision-making problem. Because of human judgment needs in many area of supplier selection such as preferences on alternatives or on the attributes of suppliers or the class number and borders supplier selection becomes more difficult and risky. 329

3 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: ABOUTVIKOR METHOD It is one of the multiple criteria decision making (MCDM)[12] models to determine the preference ranking from a set of alternatives in the presence of conflicting criteria. The Serbian name VIKOR stands for VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution was developed. This method concentrates on ranking and selecting the best from a set of alternatives, which are associated with multi-conflicting criteria. The field of multiple criteria decision making (MCDM) concerns the problems thathow decision makers should ideally do when facing multiple conflicting criteria. There are considerable methods and models have been proposed for various (MCDM) problems with respect to different perspectives and theories. In this paper, we focus on the VIKOR method which was proposed by to determine the ranking and select from a set of alternatives in the presence of conflicting criteria. The major characteristic of VIKOR is it introduces the multiple criteria ranking index based on the particular measure of closeness to the ideal solution to derive the preference rankingof alternatives. Recently, VIKOR has been widely applied for dealing with (MCDM) problems ofvarious fields, such as location selection [13], environmental policy and data envelopment analysis Steps Involved in Vikor Method The procedure of VIKOR can be described as follows. Assuming that each alternativeis evaluated according to each criterion function, the compromise ranking could be performed by comparing the measure of regret (i.e., closeness to the ideal alternative). The multi-criteria measure for compromise ranking is developed from the L p norm used as an aggregating function in a compromise programming method [14,15]. The various J alternatives are denoted as A 1, A 2, A 3,.., A j. or alternative a j, the rating ofthe ith criterion is denoted by f ji, i.e., f ji is the value ofith criterion function for the alternative a j and n is the number of criteria. The levels of regret in VIKOR can be defined as: L p,j = { n * _ i=1 [w i ( f i f ji ) / ( f * - i - f i )] p } 1/p, 1 p ; j = 1,2,.J, (2.1) wherel 1,j is defined as the maximum group utility and, L,j is defined as the minimum individual regret of the opponent. Steps involved in this method are:- Step 1. Compute the positive-ideal solutions (best) value f j * and negative-ideal solutions(worst) value f - jfor all criterion ratings.here, j = 1,2, 3..., n. Step 2. Compute the values of S i and R i (i= 1, 2, 3...,m), by using the relations: S i = n j=1 w j (f j * -f ij ) / (f j * - f j - ),. (2.2) R i = max [ w j (f j * - f ij ) / (f j * - f j * )],. (2.3) Here, S i is the aggregated value of ith alternatives with a maximum group utility and R i is the aggregated value of ith alternatives with a minimum individual regret of opponent. wjis the fuzzy weighted average of each criterion. Step 3. Compute the values Q i for i= 1, 2, 3,...,m with the relation, Q i = υ (S i S * ) / ( S - - S * ) + ( 1 - υ ) (R i R * ) / (R - - R * ).. (2.4) andν is a weight for the strategy of maximum group utility, and ν =0.5 where as 1 ν is the weight of individual regret. The compromise can be selected with voting by majority (ν>0.5), with consensus (ν= 0.5), with veto (ν <0.5). Step 4. Rank the alternatives by sorting each S, R, and Q values in ascending order. Step 5. If the following two conditions are satisfied simultaneously, then the scheme with minimum value of Q in ranking is considered the optimal compromise solution. Such as, C1. The alternative Q(A (1) )has an acceptable advantage,in other words, Q(A (2) ) Q(A (1) ) 1/ (m-1) (2.5) Where, A (2) is the alternative with the second position in the ranking list by and m is the number of alternatives. C2. The alternative Q(A (1) )is stable within the decision making process, in other words, it is also best ranked in S i and R i. If condition C 1 is not satisfied, Q(A (m) ) Q(A (1) ) < 1/(m-1) (2.6) 330

4 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: Then alternatives A (1), A (2).A (m) all are the same compromise solution, there is no comparative advantage of A (1) from others. But for the case of maximum value, the corresponding alternative is the compromise (closeness) solution. If condition C 2 is not satisfied, the stability in decision making is deficient while A (1) has a comparative advantage. Therefore, A (1) and A (2) has the same compromise solution. Step 6. Select the best alternative by choosing Q (A (m) ) as a best compromise solution with the minimum value of Q i and must have to satisfy with the above conditions. 3. UZZY SET THEORY Usually, it is very difficult to solve the problems due to the involvement of the subjectivity and uncertainty situation in the multi-criteria decision making process. Moreover, decision makers are frequently faced with confusions, and difficulties while dealing with this kind of decision making process. To overcome this vagueness, ambiguity and subjectivity of the human judgment process, fuzzy set theory has been introduced [16]. Decision makers express their opinions in terms of linguistic scales. Linguistic data s are converted into fuzzy numbers with the help of different membership functions. A set theory has become a helpful tool for mechanizing human activities with uncertainty based information. This theory is incorporated with a paper because vagueness kind information related parameters present in this supplier selection problem Definition of uzzy Sets: A fuzzy set à in a universe of discourse X is characterized by a membership function µ à (X) which associates with each element x in X a real number in the interval (0,1). The function value µ à (X) is termed the grade of membership of x in Ã[17] Definition of uzzy Numbers: A fuzzy number is a fuzzy subset in the universe of discourse X that is both convex and normal. ig. 3.1 shows a fuzzy number à in the universe of discourse X that conforms to this definition [17] Definition of Linguistic Variable: ig A fuzzy number à A linguistic variable is the variable whose values are not expressed in numbers but words or sentences in a natural or artificial language, i.e., in terms of linguistic [16].The concept of a linguistic variable is very useful in dealing with situations, which are too complex or not well defined to be reasonably described in conventional quantitative expressions [18] or example, weight is a linguistic variable whose values are very low, low, medium, high, very high, etc. uzzy numbers can also represent these linguistic values uzzy Number A fuzzy number is an generalization of a regular, real number in the sense that it does not refer to one single value but rather to a connected set of possible values, where each possible value has its own weight between 0 and 1. This weight is called the membership function. 331

5 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: , if x ϵ A μ x =. (3.1) 0, otherwise Here, {0, 1}is called an evaluation set and it is permitted to be a real interval [0, 1] for the continuous mapping membership function. Where µ(x)membership function between range of 0 to Triangular uzzy Number A positive triangular fuzzy number (PTN) is à and that can be defined as shown in ig The membership function is defined as:- ig. 3.2 A triangular fuzzy number à µ à (x) = (x a)/(b a) if a x b (c x)/ c b ifb x c.(3.2) 0, otherwise Based on extension principle, the fuzzy sum and fuzzy subtraction of any two triangular fuzzy numbers are also triangular fuzzy numbers; but the multiplication of any two triangular fuzzy numbers is only approximate triangular fuzzy number[16]. Lets have a two positive triangular fuzzy numbers, such as à 1 = (a 1, b 1, c 1 ) and à 2 = (a 2, b 2, c 2 ) a positive real number r = (r, r, r), some algebraic operations can be expressed as follows: à 1 + à 2 = (a 1 +a 2,b 1 +b 2, c 1 +c 2 ) (3.3) à 1 à 2 = (a 1 -a 2, b 1 -b 2, c 1 -c 2 ). (3.4) à à = (a 1 a 2, b 1 b 2, c 1 c 2 )....(3.5) r à 1 = (ra 1, rb 1, rc 1 ) à 1 / à 2 = (a 1 /c 2, b 1 /b 2, c 1 /a 2 ).(3.6)..(3.7) 4. Methodology Adopted 332

6 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: ig 4:- flow chart of methodology In this we adopted the method based on the concept of fuzzy set theory and vikor method, we have surveyed 3 companies viz. 1.) Kwality ball. co.lt 2.) Eurasia polychem. Co. ltd 3.) Techno mac plast and from there we collected data about their supply chain, and we mainly focused on 3 criteria i.e cost, material handling and duration. Then proposed fuzzy vikor method has been applied to find the best compromise solution under the multi-person multi-criteria decision making supplier selection problem. Usually, decision making problems are dealing with some alternatives which can be ranked, with respect to distinct criteria. Ratings of the alternatives and the weights of each criterion are the two most significant data which can affect the results of decision making problems. Therefore, the proposed methodology has been used here, to calculate the definite weight of criteria and ranking of the alternatives. In this paper, the importance weights of various criteria and ratings of qualitative criteria are measured as linguistic variables because linguistic assessment can only have the capability to approximate the subjective judgment through a decision maker s opinion. Moreover, linear triangular membership functions are considered for capturing the vagueness of these linguistic assessments. The proposed algorithm consists of the following steps: Step 1. irst of all we make a list of feasible alternatives, find the evaluation criteria, and constitute a group of decision makers. Suppose, there are k decision makers (D t, t = 1, 2,..., k), who are responsible for assessing m alternatives (A i, i= 1, 2,...,m), with respect to the importance of each of the n criteria, (C j = 1, 2,..., n). Step 2. Then with help of positive triangular fuzzy number we identify appropriate linguistic variables.linguistic variables are used to calculate the importance weights of criteria and the ratings of the alternatives with respect to distinct criteria. or example, linguistic variable Very High (VH) which can be defined by a triangular fuzzy number (0.75, 1, 1). Step 3. After that we construct a fuzzy decision table by pulling the decision maker s opinions to get the aggregated fuzzy weight of the criteria, and the aggregated fuzzy rating of alternatives. Let kis the number of decision makers in a group and, the aggregated fuzzy weight ( Ŵ j ) with respect to each criterion can be calculated as :- Ŵ j = 1 [ Ŵ j1 +Ŵ j2 +..+Ŵ jk ] (4.1) k And also the aggregated fuzzy ratings ( xij) of alternatives with respect to each criterion can be calculated as: X ij = 1 k [ x ij1+ x ij2 +.+ x ijk ].(4.2) Step 4. We have to find the crisp value from the aggregated data. Step 5. Determine best crisp value (f j * ) and worst crisp value (f j - ) for all criterion ratings, (j = 1, 2,..., n) by using the relations: (f j * = max x i ) (4.3) (f j - = min x i ).(4.4) Step 6. Compute the values S i and R i using the relations (2.2) and (2.3) respectively Step 7. Compute the values Q i using the relation (2.4). Step 8. Rank the alternatives by sorting each S, R,and Q values in ascending order. Step 9. Select the best alternatives as a compromise solution by referring step 5 5. CASE STUDY Supplier selection is an important part of the business as well as a production strategy for industrial organizations. Selection of best supplier enhances the quality and economic growth of enterprise but, still it is a difficult task to select an appropriate supplier. Therefore, the proposed model has been used to evaluate and select the most suitable supplier of a manufacturing industry.the proposed supplier selection approach has been made in the following steps:- Step 1. There are three suppliers such as, S 1, S 2 and S 3 participating in the selection process. These are the three qualitative criteria used to evaluate the suppliers:- C 1 : cost of goods, 333

7 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: C 2 : handling of products, C 3 : Duration. Three decision makers D 1, D 2 and D 3 have been grouped to resolve the problems of the entire selection process. Step 2. Now decision makers have been using the five linguistic variables for weighting and the corresponding fuzzy numbers of linguistic variables for weights are shown in Table 5.1 Table 5.1. Linguistic variables for weights. V e r y L o w ( V L ) ( 0, 0, ) L o w ( L ) ( 0, , 0. 5 ) M e d i u m ( M ) (0.25,0.5, 0.75) H i g h ( H ) ( 0. 5, , 1 ) V e r y H i g h ( V H ) ( , 1, 1 ) Step 3.The five linguistic variables for rating of suppliers, and their corresponding fuzzy numbers of linguistic variables are shown in table 5.2 Table 5.2. Linguistic variables for ratings. V e r y P o o r ( V P ) ( 0, 0, ) P o o r ( P ) ( 0, , 0. 5 ) a i r ( ) ( , 0. 5, ) o o d ( ) ( 0. 5, , 1 ) V e r y o o d ( V ) ( , 1, 1 ) Step 4. Then, the decision makers use the linguistic weighting variables to assess the importance weight of each criterion as shown in Table 5.3 Table 5.3. Importance weight of criteria from three decision makers. C r i t e r i a D 1 D 2 D 3 C 1 V H H V H C 2 H V H H C 3 M V H V H Step 5. Also, they have used the linguistic ratings to rate the alternatives presented in Table 5.4 Table 5.4. Ratings of five suppliers under each criterion in terms oflinguistic variables determined by DMs. DMs Suppliers C r i t e r i a C 1 C 2 C 3 334

8 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: D 1 S 1 S 2 S 3 D 2 S 1 S 2 S 3 V V V V D 3 S 1 S 2 S 3 V Step 6.The calculated fuzzy numbers of importance weightsare tabulated in Table 5.5, i.e numerical given for each linguistic variables are tabulated below. V Table 5.5. Importance weights of criteria in terms of fuzzy numbers for each criterion. C 1 C 2 C 3 D 1 ( , 1, 1 ) (0.50,0.75,1) D 2 (0.50,0.75,1) ( , 1, 1 ) ( , 1, 1 ) D 3 ( , 1, 1 ) (0.50,0.75,1) ( , 1, 1 ) Step 7. The calculated fuzzy numbers of importanceratings are given in Table5.6 Table 5.6. Rating of each supplier under each criterion in terms of fuzzy numbers. S u p p l i e r S 1 C r i t e r i a C 1 C 2 C 3 D 1 D 2 D 3 ( , , ) ( , , ) ( , 0. 5, ) (0.25,0.5,0.75) (0.25,0.5,0.75) D 1 D 2 D 3 ( , , ) (0.75,1.00,1.00) S 2 ( , , ) (0.75,1.00,1.00) ( , , ) D 1 D 2 D 3 S 3 ( , , ) ( , , ) ( , , ) (0.75,1.00,1.00) Step 8. The aggregated fuzzy weight (W j ) of each criterion and aggregated fuzzy ratings (X ij ) of each criterion with respect to the suppliers are calculated by using the relation (3.3) respectively. Then, construct a fuzzy decision matrix by putting these aforesaid data and shown in Table 5.7. Table 5.7. uzzy decision matrix. C r i t e r i a C 1 C 2 C 3 Weight (0. 67, 0. 92, ) (0.58,0.83,1.00) (0.58,0.83,0.92) S 1 (0. 33, 0. 58, ) (0.50,0.75,0.92) S 2 (0. 50, 0. 75, ) (0.67,0.92,1.00) (0.42,0.67,0.92) S 3 (0. 42, 0. 67, ) (0.42,0.67,0.92) (0.50,0.75,0.92) Step 9. Compute the crisp values of decision matrix and weight of each criterion and presented in Table

9 ISSN: , Volume 4, Issue 7, July- 2016, Impact actor: Table 5.8. Crisp values for decision matrix and weight of each criterion. C r i t e r i a C 1 C 2 C 3 W e i g h t s S S S Step 10. These are the best and worst values of all criterion ratings listed below: f * 1 = 0.72, f * 2 =0.86, f * 3 =0.72, f - 1 = 0.58, f - 2 =0.67, f - 3 =0.50. Step 11. Compute the values of S, R and Q for all suppliers and presented in Table 5.9. Table 5.9. The values of S, R and Q for all suppliers. S u p p l i e r S 1 S 2 S 3 S R Q Step 12. Ranking of suppliers by S, R and Q in ascending order are shown in Table Table The ranking of the suppliers by S, R and Q in ascending order. Ra n k i n g s u p pl i er s B y S S 2 S 3 S 1 B y R S 2 S 3 S 1 B y Q S 2 S 3 S 1 s Step 13. rom Table 5.10, it has been shown that the suppliers S 2 is best ranked by Q and also both C 1 and C 2 conditions are satisfied, means(q S2 Q S1 ) 1/(3 1) and S 2 is best ranked by R and S also. Therefore, S 2 is the best selected supplier for the best compromise solution. CONCLUSION Supplier selection is a part of supply chain management which is used in the upstream of the production process and affecting all the areas of an organization. In this an efficient method has been proposed to solve the supplier selection problem and select the best supplier through a multi criteria group decision making process under a fuzzy environment. In a decision making process, the decision makers are unable to express their opinions exactly in numerical values due to imprecision in the subjective judgment of decision-makers. In order to deal with such problems fuzzy set theory has been implemented and the evaluations are expressed in linguistic terms. In this research a new MDCM approach, VIKOR under a fuzzy environment has been implemented to deal with both qualitative and quantitative criteria and a suitable supplier has been selected successfully. The outranking order of suppliers and rating of suppliers can easily be determined by using this method. inally, the proposed method has been seemed simple, flexible and systematic approach and can be applied in different types of decision making problems. REERENCES [1]. Simon, H.A., The New Science of Management Decision, Prentice Hall PTR, New Jersey, Vol. 8, , (1977) [2]. Keeney, R.L., and Raiffa, H., Decision with Multiple Objectives: Preferences and ValueTradeoffs, John Wiley and Sons, New York, Vol.3, , (1976) [3]. Kleindorfer, P.R., Kunreuther, H.C., Schoemaker, P.J.H., Decision Sciences: An Integrative Perspective, Cambridge University Press, Cambridge,Vol. 20, , (1993) 336

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