An Integrated Approach of Fuzzy TOPSIS and Graph Theory with Confidence Analysis on Personnel Selection

Abstract

This paper proposes a methodology based on combined approach of graph theory with Fuzzy TOPSIS and equipped with confidence analysis in solving a multi-criteria decision-making problem (MCDM). The theoretical background of some graph theories is reviewed and the basic principle of fuzzy TOPSIS is presented. Graph-theoretic method is used to clearly depict a logical
and systematic framework in accessing the available criteria with the relative importance. The main concept of Fuzzy TOPSIS is that the chosen alternative should be the closest to the fuzzy positive ideal solution and the furthest away from the fuzzy negative ideal solution, which may be found by computing the closeness coefficient. In this paper, the fuzzy closeness coefficient with
-level set is determined. The coefficient can be represented as a fuzzy number, resulting in a more accurate fuzzy assessment of relative closeness. To show the applicability of the approach, a numerical analysis is presented to study the personnel selection based on the proposed method. It was found that the approach is able to generate a more comprehensive, systematic, and
reliable result.