Extension of the TOPSIS method for decision-making problems with fuzzy data

The aim of this paper is to extend the TOPSIS method to decision-making problems with fuzzy data, and the rating of each alternative and the weight of each criterion are expressed in triangular fuzzy numbers.

Abstract

Decision making problem is the process of finding the best option from all of the feasible alternatives. In this paper, from among multicriteria models in making complex decisions and multiple attribute models for the most preferable choice, technique for order preference by similarity to ideal solution (TOPSIS) approach has been dealt with. In real-word situation, because of incomplete or non-obtainable information, the data (attributes) are often not so deterministic, there for they usually are fuzzy/imprecise. Therefore, the aim of this paper is to extend the TOPSIS method to decision-making problems with fuzzy data. In this paper, the rating of each alternative and the weight of each criterion are expressed in triangular fuzzy numbers. The normalized fuzzy numbers is calculated by using the concept of α-cuts. Finally, a numerical experiment is used to illustrate the procedure of the proposed approach at the end of this paper.