Hybrid fuzzy MADM ranking procedure for better alternative discrimination

In this paper, we propose a hybrid fuzzy decision making approach, combining elements of fuzzy-ELECTRE and Fuzzy-TOPSIS, towards a new ranking procedure. The main objective of FETOPSIS is to offer rankings with good alternative discriminatory power to decision makers (DMs). This research work was motivated by a real case study in which multiple attribute decision making techniques were used to select the best set of investment projects for the industrial restructuring of a small oil company in Brazil. After the application of Fuzzy-TOPSIS and ELECTRE II, the obtained rankings were quite deceptive from the DMs' point of view, either to very close scores or by the excess of indifferences among alternatives. Our developed approach uses the closeness coefficients to rank the alternatives, following Fuzzy-TOPSIS, however they are computed over the normalized fuzzy concordance and discordance indexes based on the ELECTRE family. Extensive computational experiments were performed to evaluate our method. The good results obtained by FETOPSIS in the experiments, both in terms of alternative discriminatory power of rankings, and eliminating ranking reversal cases, gave us the confidence to apply the method in the real case. The DMs praised the developed approach, since the obtained rankings were more discriminatory in the alternatives than both Fuzzy-TOPSIS and ELECTRE II, making it possible to select with confidence a set of suited alternatives. HighlightsA new MADM approach, combining ELECTRE and TOPSIS elements, was introduced.We addressed the gap in the ELECTRE literature about ranking reversals.We performed an in-depth analysis of an ELECTRE based method.The developed approach improved the discrimination of alternatives.Preferred scores by DMs were obtained in a real-world application.