The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

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.

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In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: