Fuzzy Multicriteria Decision‐Making Methods: A Comparative Analysis

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 MULTIMOORA, 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.

[1]  Abbas Mardani,et al.  Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014 , 2015 .

[2]  Adel Guitouni,et al.  Tentative guidelines to help choosing an appropriate MCDA method , 1998, Eur. J. Oper. Res..

[3]  Alvydas Balezentis,et al.  MULTIMOORA-FG: A Multi-Objective Decision Making Method for Linguistic Reasoning with an Application to Personnel Selection , 2012, Informatica.

[4]  Juan Miguel Sánchez-Lozano,et al.  Evaluation of photovoltaic cells in a multi-criteria decision making process , 2012, Ann. Oper. Res..

[5]  Edmundas Kazimieras Zavadskas,et al.  Robustness of MULTIMOORA: A Method for Multi-Objective Optimization , 2012, Informatica.

[6]  Thomas L. Saaty,et al.  When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods , 2015, Int. J. Inf. Technol. Decis. Mak..

[7]  María Teresa Lamata,et al.  A comparative analysis of multi-criteria decision-making methods , 2016, Progress in Artificial Intelligence.

[8]  Serafim Opricovic,et al.  Fuzzy VIKOR with an application to water resources planning , 2011, Expert Syst. Appl..

[9]  Tomas Baležentis,et al.  A Survey on Development and Applications of the Multi‐criteria Decision Making Method MULTIMOORA , 2014 .

[10]  V. Tummala,et al.  A comparative study of multiattribute decision making methodologies , 1990 .

[11]  Evangelos Triantaphyllou,et al.  Development and evaluation of five fuzzy multiattribute decision-making methods , 1996, Int. J. Approx. Reason..

[12]  María Teresa Lamata,et al.  A Modification of the Index of Liou and Wang for Ranking Fuzzy Number , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[13]  Simon French,et al.  A manifesto for the new MCDA era , 1993 .

[14]  Stelios H. Zanakis,et al.  Multi-attribute decision making: A simulation comparison of select methods , 1998, Eur. J. Oper. Res..

[15]  Teemu Tiainen,et al.  Comparative study of multiple criteria decision making methods for building design , 2012, Adv. Eng. Informatics.

[16]  Patrick T. Hester,et al.  An Analysis of Multi-Criteria Decision Making Methods , 2013 .

[17]  Anita Schöbel,et al.  On the Similarities of Some Multi‐Criteria Decision Analysis Methods , 2011 .

[18]  Tien-Chin Wang,et al.  Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment , 2007, Expert Syst. Appl..

[19]  Edmundas Kazimieras Zavadskas,et al.  State of art surveys of overviews on MCDM/MADM methods , 2014 .

[20]  Jurgita Antucheviciene,et al.  A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection , 2015, Int. J. Comput. Commun. Control.

[21]  L. Anojkumar,et al.  Comparative analysis of MCDM methods for pipe material selection in sugar industry , 2014, Expert Syst. Appl..