Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection

Abstract

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