Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection

By Mehdi Keshavarz-Ghorabaee, Edmundas Kazimieras Zavadskas, Maghsoud Amiri, Zenonas Turskis. International Journal of Computers Communications & Control, 2016

338 citations recorded by the source corpus.

In the real-world problems, we are likely confronted with some alternatives that eed to be evaluated with respect to multiple conflicting criteria. Multi-criteria ecision-making (MCDM) refers to making decisions in such a situation. There are any methods and techniques available for solving MCDM problems. The evaluation ased on distance from average solution (EDAS) method is an efficient multi-criteria ecision-making method. Because the uncertainty is usually an inevitable part of he MCDM problems, fuzzy MCDM methods can be very useful for dealing with the eal-world decision-making problems. In this study, we extend the EDAS method o handle the MCDM problems in the fuzzy environment. A case study of supplier election is used to show the procedure of the proposed method and applicability of t. Also, we perform a sensitivity analysis by using simulated weights for criteria to xamine the stability and validity of the results of the proposed method. The results f this study show that the extended fuzzy EDAS method is efficient and has good tability for solving MCDM problems.

Subfields: Control and Systems Engineering, Management of Technology and Innovation, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0480eab1de196f7156c41caff3dacc25954c97d2

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In Control and Systems Engineering

In Management of Technology and Innovation

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Citations in the corpus

Pythagorean fuzzy set: state of the art and future directions (2017, background): “That is to say, we can deepen the present work (Gou et al. 2016), discuss some interesting properties and apply them to solve real-life problems, not limit to fundamental research.”
A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments (2017, methodology): “methods [225] can be adapted to the development of hybrid MCDM methods such as ARAS, EDAS [226-229], Investment”