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Enhancing the discrimination of alternatives in fuzzy-topsis

Abstract:

Fuzzy-TOPSIS is one of the most widely applied methods for solving multi-attribute decision making problems. However, an analysis of academic and real-life applications of this method has pointed out that the final alternative scores are very close, with little dispersion among them, making it difficult for the decision makers' to choice/ranking the alternatives. The main objective of this paper is to enhance the ability of Fuzzy-TOPSIS to discriminate alternatives, making it easy for a decision maker to select or ranking alternatives. To achieve this, we redefined the computation of the positive and negative ideal solution of the classical TOPSIS method as a combination of the fuzzy concordance and discordance indexes from Fuzzy-ELECTRE. The proposed model was validated in a real case study, and further compared with Fuzzy-ELECTRE, using simulation experiments, and Fuzzy-TOPSIS, using results from four recent papers published in the literature. The results obtained show that the proposed method improved the ranking and sorting of the alternatives for all analyzed cases, considering ranking dispersion, global interval range of the scores, and the difference between the first and second best alternatives. The main justification for this behavior is the partial non-compensatory nature of our method, introduced by incorporating some ELECTRE's elements into Fuzzy- TOPSIS.