Abstract

 
 
 One of the techniques used to support decisions in uncertain environments is the Fuzzy TOPSIS method. However, from crisp data, this method considers only one fuzzy set in their analysis, besides being a strictly mathematical optimization technique. This article proposes extensions to the original Fuzzy TOPSIS, exploring two distinct versions: to increase the method with the necessary resources for the mathematics process to consider the membership values of the input data in more than one fuzzy set and to aggregate to method the empiric knowledge of an expert represented through fuzzy rules. In such case, the method, named by Fuzzy F-TOPSIS (Fuzzy Flexible TOPSIS), is proposed with the objective of improving the Fuzzy TOPSIS ability to deal with uncertainty through the combination of the mathematical process involved in the original Fuzzy TOPSIS with the expert empirical knowledge. A case study is presented to validate the proposal.
 
 
Highlights
The use of fuzzy logic and of fuzzy sets theory applied to the decision making process was proposed initially in [1]
In order to improve their ability to deal with vagueness, in this paper we propose adding to the Fuzzy TOPSIS an attribute *0J that represents the evaluation of a Fuzzy Rules Based System
Among all the weighted fuzzy sets calculated by Equation (2), for each of *0J attributes involved in the problem, the largest coordinated and will be considered in defining of the FPIS
Summary
The use of fuzzy logic and of fuzzy sets theory applied to the decision making process was proposed initially in [1]. Defined the fuzzy matrix with the fuzzy sets I5;< and 89;, we must calculate the weighted matrix using Equation (2), taking into account the importance degree (89;) of each attribute and the set fuzzy (I5;
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