An Effective Extension of Fuzzy TOPSIS Method for Hierarchically Structured Group Decision Making Problems

Abstract

An extension of Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is proposed to solve hierarchically structured and Multiple Attribute Decision Making (MADM) problems in individual and group decision making environments. Firstly, the data collection phase is enhanced by the use of fuzzy Linguistic Preference Relations (LinPreRa) method to obtain consistent judgments via reducing data collected from decision makers. After simplifying the process of data collection, we aggregate the judgments of decision makers related to importance weights and performance evaluations to obtain weighted normalized fuzzy performance matrix. As a classical step of TOPSIS, fuzzy distances are calculated to negative ideal and positive ideal solutions in order to obtain final ranking (regarding the scores of fuzzy closeness coefficients) for different risk attitudes. We adapted Chakraborty and Chakraborty (2006)'s fuzzy distance method by employing by means of a-cut intervals. A numerical study and a real-life case are provided to demonstrate the proposed approach and the results are compared with previous studies (Wang et al. (2009) and Demirel et al.(2010)) to illustrate that proposed approach is both robust and efficient.