Fuzzy Multicriteria Decision-Making: A Literature Review (original) (raw)

Fuzzy Multicriteria Decision-Making Methods: A Comparative Analysis

International Journal of Intelligent Systems, 2016

Given a multicriteria decision-making problem, an obvious question emerges: Which method should be used to solve it? Although some efforts had been made, the question remains open. The aim of this contribution is to compare a set of multicriteria decision-making methods sharing three features: same fuzzy information as input data, the need of a data normalization procedure, and quite similar information processing. We analyze the rankings produced by fuzzy MULTI-MOORA, fuzzy TOPSIS (with two normalizations), fuzzy VIKOR, and fuzzy WASPAS with different parameterizations, over 1200 randomly generated decision problems. The results clearly show their similarities and differences, the impact of the parameters settings, and how the methods can be clustered, thus providing some guidelines for their selection and usage.

A new algorithm for fuzzy multicriteria decision making

International Journal of Approximate Reasoning, 1992

An algorithm for fuzzy multicriteria decision making is developed that allows the use of linguistic ratings as well as numeric ratings. This algorithm is based on and maintains the advantages of weighted-average rating methods and implied conjunction methods. The proposed algorithm is tested against another method and is shown to be precise and efficient.

Models for Fuzzy Multicriteria Decision Making Based on Fuzzy Relations

2000

The paper presents models and corresponding algorithms for solving fuzzy multicriteria decision making problems. The models use or transform the initial information to fuzzy preference relations by each criterion. These relations possess required properties to solve the problems of choice or ordering of the alternatives. The weights of the criteria are real numbers or weighting functions.

Fuzzy multiple criteria decision making: Recent developments

Fuzzy Sets and Systems, 1996

Multiple Criteria Decision Making (MCDM) shows signs of becoming a maturing field. There are four quite distinct families of methods: (i) the outranking, (ii) the value and utility theory based, (iii) the multiple objective programming, and (iv) group decision and negotiation theory based methods. Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible; the most important methods are reviewed and a novel approach -interdependence in MCDM -is introduced.

A New Software Development for Fuzzy Multicriteria Decision‐Making

Technological and Economic Development of Economy, 2009

In this paper, software for Fuzzy Multiple Criteria Decision Making (FMCDM) problems has been developed and tested on two real problems. FMCDM methods are widely used when imprecise data or linguistic variables exist in the problem. Using FMCDM methods may help improve decision-making problems and lead to more accurate models. Although these methods are more involved in terms of computing due to fuzzy calculations in MCDM algorithms, fuzziness offers advantages over classical algorithms. Thus appropriate software is of great importance in applying FMCDM methods. The major aim of this study is to develop software and to test it on two real military problems which are solved by an ideal points algorithm and an outranking method. The results and outputs are discussed with sensitivity analyses.

Fuzzy Systems for Multicriteria Decision Making

Clei Electronic Journal, 2010

One of the techniques used to support decisions in uncertain environments is the Fuzzy TOPSIS method. However, from crisp data, this method considers only one fuzzy set in their analysis, besides being a strictly mathematical optimization technique. This article proposes extensions to the original Fuzzy TOPSIS, exploring two distinct versions: to increase the method with the necessary resources for the mathematics process to consider the membership values of the input data in more than one fuzzy set and to aggregate to method the empiric knowledge of an expert represented through fuzzy rules. In such case, the method, named by Fuzzy F-TOPSIS (Fuzzy Flexible TOPSIS), is proposed with the objective of improving the Fuzzy TOPSIS ability to deal with uncertainty through the combination of the mathematical process involved in the original Fuzzy TOPSIS with the expert empirical knowledge. A case study is presented to validate the proposal.

A method for solving fuzzy multicriteria decision problems with dependent criteria

Fuzzy Optimization and Decision Making, 2010

We propose a new multi-criteria decision making (MCDM) method based on fuzzy pair-wise comparisons and a feedback between the criteria. The evaluation of the weights of criteria, the variants as well as the feedback between the criteria is based on the data given in pair-wise comparison matrices. Extended arithmetic operations with fuzzy numbers are used as well as ordering fuzzy relations to compare fuzzy outcomes. An illustrating numerical example is presented to clarify the methodology. A special SW-Microsoft Excel add-in named FVK was developed for applying the proposed method. Comparing to other software products, FVK is free, able to work with fuzzy data and utilizes capabilities of widespread spreadsheet Microsoft Excel.

A Survey on Multi Criteria Decision Making Methods and Its Applications

Multi Criteria Decision Making (MCDM) provides strong decision making in domains where selection of best alternative is highly complex. This survey paper reviews the main streams of consideration in multi criteria decision making theory and practice in detail. The main purpose is to identify various applications and the approaches, and to suggest approaches which are most robustly and effectively useable to identify best alternative. This survey work also addresses the problem in fuzzy multi criteria decision making techniques. Multi criteria decision making have been applied in many domains. MCDM method helps to choose the best alternatives where many criteria have come into existence, the best one can be obtained by analyzing the different scope for the criteria, weights for the criteria and the choose the optimum ones using any multi criteria decision making techniques. This survey provides the comprehensive developments of various methods of FMCDM and its applications.

Fuzzy Multi-Criteria Decision Making

Springer Optimization and Its Applications, 2008

Aims and Scope Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics and other sciences. The series Optimization and Its Applications publishes undergraduate and graduate textbooks, monographs and state-of-the-art expository works that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network flow problems, stochastic optimization, optimal control, discrete optimization, multiobjective programming, description of software packages, approximation techniques and heuristic approaches.

Multicriteria Decision Making by Fuzzy Relations and Weighting Functions for the Criteria

A multicriteria decision making problem with criteria giving fuzzy relations between the couples of alternatives is considered. The importance of each of the criteria is given as weighting function depending on the membership degrees of the corresponding fuzzy relation. Transformed membership degrees with the help of these weighting functions are used in the aggregation procedure for fusing the relations. The properties of the weighted relations, required to decide the problems of choice, ranking or clustering of the alternatives' set are proved. An illustrative numerical example solving the problem of alternatives' ranking is presented as well.