Fuzzy Multi-criteria Decision-making Problems Research Articles

Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets

Out of several generalizations of fuzzy set theory for various objectives, the notions introduced by Atanassov (1983) and Atanassov and Gargov (1989) in defining intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets are interesting and very useful in modeling real life problems. Ranking of interval-valued intuitionistic fuzzy sets plays a vital role in decision-making, data analysis, artificial intelligence and socioeconomic system and it was studied in Xu (2007c), Xu and Chen (2007a) and Ye (2009). In this paper a new method for ranking interval-valued intuitionistic fuzzy sets has been introduced and studied. The method is illustrated by numerical examples and compared with other methods. And then a new method for handling multi-criteria fuzzy decision-making problems based on interval-valued intuitionistic fuzzy sets is presented in which criterion values for alternatives are interval-valued intuitionistic fuzzy sets. The method proposed here can provide a useful way to efficiently help the decision-maker to make his decision. An illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets

A multicriteria fuzzy decision-making method based on weighted correlation coefficients using entropy weights is proposed under interval-valued intuitionistic fuzzy environment for the some situations where the information about criteria weights for alternatives is completely unknown. To determine the entropy weights with respect to a decision matrix provided as interval-valued intuitionistic fuzzy sets (IVIFSs), we propose two entropy measures for IVIFSs and establish an entropy weight model, which can be used to determine the criteria weights on alternatives, and then propose an evaluation formula of weighted correlation coefficient between an alternative and the ideal alternative. The alternatives can be ranked and the most desirable one(s) can be selected according to the values of the weighted correlation coefficients. Finally, two applied examples demonstrate the applicability and benefit of the proposed method: it is capable for handling the multicriteria fuzzy decision-making problems with completely unknown weights for criteria.

A Novel Measured Function for MCDM Problem Based on Interval-Valued Intuitionistic Fuzzy Sets

Several papers have presented measured function to handle multi-criteria fuzzy decision-making problems based on interval-valued intuitionistic fuzzy sets. However, in some cases, the proposed function cannot give sufficient information about alternatives. Consequently, in this paper, we will overcome previous insufficient problem and provide a novel accuracy function to measure the degree of the interval-valued intuitionistic fuzzy information. And a practical example has been provided to demonstrate our proposed approach. In addition, to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed for decision makers to make decisions more efficiently.

Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR

The decision of strategic information system/information technology (IS/IT) outsourcing requires close attention to the evaluation of supplier/vendor selection process because the selection decision involves conflicting multiple criteria and is replete with complex decision-making problems. Selecting the most appropriate suppliers/vendors is considered an important strategic decision that may impact the performance of outsourcing engagements. The purpose of this study is to provide a more efficient delivery approach for evaluating and assessing possible suppliers/vendors. Using the fuzzy VIKOR method, this study provides a rational and systematic process for developing the best alternative and compromise solution under each of the selection criteria. The study's finding offers an important reference for resolving fuzzy multi-criteria decision-making problems.

An enhanced method and its application for fuzzy multi-criteria decision making based on vague sets

Ye [Ye Jun. Improved method of multicriteria fuzzy decision making based on vague sets. Computer-Aid Design 2007;39:164–9] presented an improved method to handle multi-criteria fuzzy decision-making problems based on vague set theory. He/She provided some functions to measure the degree of suitability of each alternative with respect to a set of criteria presented by vague values. However, in some cases, these functions do not give sufficient information about alternatives. Therefore, in this paper, an enhanced method is provided to measure the accuracy membership of each alternative so as to give additional information for the decision maker. In addition, to making computing and ranking results easier and to increase the recruiting productivity, a computer-based decision-support system is also developed, which may help to make a decision more efficiently.

A fuzzy clustering methodology for linguistic opinions in group decision making

The objective of this paper is to automate a decision-aid tool, which provides homogeneous clusters from a set of heterogeneous opinions for a particular criterion to evaluate an item under group decision scheme. In such situation, decision-making by a group of experts becomes more realistic and consistent while they provide more or less homogeneous responses. But in real practice, the homogeneity among the opinions for a specific criterion to evaluate an item is not maintained due to the diversity among the experts’ several cognitive factors as well as biasness. As a result, the group's overall effectiveness is suffered and making a true decision becomes difficult as well as sometimes confusing. In order to avoid the heterogeneity among the opinions (fuzzy numbers), we propose here a fuzzy clustering methodology based on a fuzzy distance measure. Also a ranking index is introduced on the basis of Ordered Weighted Average (OWA) operator. Finally, a fuzzy multi-criteria decision-making problem on a flight simulator software development project is considered here to employ the proposed technique. The results are discussed and compared.

Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets

In this paper, a new method for handling multicriteria fuzzy decision-making problems based on intuitionistic fuzzy sets is presented. The proposed method allows the degrees of satisfiability and non-satisfiability of each alternative with respect to a set of criteria to be represented by intuitionistic fuzzy sets, respectively. Furthermore, the proposed method allows the decision-maker to assign the degrees of membership and non-membership of the criteria to the fuzzy concept “importance.” The method proposed here can provide a useful way to efficiently help the decision-maker to make his decision.

A Prioritized Information Fusion Method for Handling Fuzzy Decision-Making Problems

Although Yager has presented a prioritized operator for fuzzy subsets, called the non-monotonic operator, it can not be used to deal with multi-criteria fuzzy decision-making problems when generalized fuzzy numbers are used to represent the evaluating values of criteria. In this paper, we present a prioritized information fusion algorithm based on the similarity measure of generalized fuzzy numbers. The proposed prioritized information fusion algorithm has the following advantages: (1) It can handle prioritized multi-criteria fuzzy decision-making problems in a more flexible manner due to the fact that it allows the evaluating values of criteria to be represented by generalized fuzzy numbers or crisp values between zero and one, and (2) it can deal with prioritized information filtering problems based on generalized fuzzy numbers.

A NEW METHOD FOR HANDLING MULTICRITERIA FUZZY DECISION-MAKING PROBLEMS USING FN-IOWA OPERATORS

We use fuzzy numbers to extend the traditional induced ordered weight averaging (IOWA) operator to present the fuzzy-number IOWA (FN-IOWA) operator, wherein fuzzy numbers are used to describe the argument values and the weights of the FN-IOWA operator, and the aggregation results are obtained by using fuzzy-number arithmetic operations. We also present a new method for ranking fuzzy numbers. Based on the proposed FN-IOWA operator and the proposed ranking method of fuzzy numbers, we present a new algorithm to deal with multicriteria fuzzy decision-making problems. The proposed algorithm can deal with multicriteria fuzzy decision-making problems in a more intelligent and more flexible manner.

Fuzzy multiple-criteria decision-making approach for industrial green engineering.

This paper describes a fuzzy hierarchical analytic approach to determine the weighting of subjective judgments. In addition, it presents a nonadditive fuzzy integral technique to evaluate a green engineering industry case as a fuzzy multicriteria decision-making (FMCDM) problem. When the investment strategies are evaluated from various aspects, such as economic effectiveness, technical feasibility, and environmental regulation, it can be regarded as an FMCDM problem. Since stakeholders cannot clearly estimate each considered criterion in terms of numerical values for the anticipated alternatives/strategies, fuzziness is considered to be applicable. Consequently, this paper uses triangular fuzzy numbers to establish weights and anticipated achievement values. By ranking fuzzy weights and fuzzy synthetic utility values, we can determine the relative importance of criteria and decide the best strategies. This paper applies what is called a lambda fuzzy measure and nonadditive fuzzy integral technique to evaluate the synthetic performance of green engineering strategies for aquatic products processors in Taiwan. In addition, we demonstrate that the nonadditive fuzzy integral is an effective evaluation and appears to be appropriate, especially when the criteria are not independent.

Multicriteria fuzzy decision-making problems based on vague set theory

Chen et al. (Fuzzy Sets and Systems 67 (1994) (163–172)) present some techniques for handling multicriteria fuzzy decision-making problems based on vague set theory. They provide some functions to measure the degree of suitability of each alternative with respect to a set of criteria presented by vague values. However, in some cases, these functions do not give sufficient information about alternatives. In this paper, we provide new functions to measure the degree of accuracy in the grades of membership of each alternative with respect to a set of criteria represented by vague values. The proposed functions give additional information about alternatives. The techniques proposed in this paper can provide more useful way than those of Chen to efficiently help the decision-maker to make his decision.

Handling multicriteria fuzzy decision-making problems based on vague set theory

Abstract New techniques for handling multicriteria fuzzy decision-making problems based on vague set theory are presented. The proposed techniques allow the degrees of satisfiability and non-satisfiability of each alternative with respect to a set of criteria to be presented by vague values. Furthermore, the proposed techniques allow the decision-maker to assign a different degree of importance to each criteria. The techniques proposed in this paper can provide a useful way to efficiently help the decision-maker to make his decisions.

A NEW METHOD FOR HANDLING MULTICRITERIA FUZZY DECISION-MAKING PROBLEMS

This paper presents a new method for handling multicriteria fuzzy decision-making problems, in which the characteristics of the alternatives are represented by interval-valued fuzzy sets, and some techniques are developed to calculate the degree of similarity between interval-valued fuzzy sets. The proposed method is more flexible than the one we presented earlier (Chen et al., 1989) because it allows the criteria values of the alternatives to be represented by real intervals rather than crisp real values between zero and one.