A New Divergence Measure based on Fuzzy TOPSIS for Solving Staff Performance Appraisal

Abstract

Various divergence measure methods have been used in many applications of fuzzy set theory for calculating the discrimination between two objects. This paper aims to develop a novel divergence measure incorporated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, along with the discussions of its properties. Since ambiguity or uncertainty is an inevitable characteristic of multi-criteria decision-making (MCDM) problems, the fuzzy concept is utilised to convert linguistic expressions into triangular fuzzy numbers. A numerical example of a staff performance appraisal is given to demonstrate suggested method's effectiveness and practicality. Outcomes from this study were compared with various MCDM techniques in terms of correlation coefficients and central processing unit (CPU) time. From the results, there is a slight difference in the ranking order between the proposed method and the other MCDM methods as all the correlation coefficient values are more than 0.9. It is also discovered that CPU time of the proposed method is the lowest compared to the other divergence measure techniques. Hence, the proposed method provides a more sensible and feasible solutions than its counterparts.

Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal

“…Microsoft software, specifically Visual C++ and Excel, were utilized for evaluating the suggested method and conducting the necessary statistical tests. To scrutinize and verify the efficacy of the proposed approach, data from [42,56] and a real case study involving staff performance reviews at educational institutes are referenced as Case Studies 1 and 2, respectively. For each case study, an expert group E = {E 1 , E 2 } was formed to evaluate the alternatives from a linguistic perspective.…”

“…This achievement is the result of an extensive review and analysis of existing measures. Secondly, the research improves the method proposed in [42] by applying fuzzy α-cut to the MCDM problem of selecting the most appropriate candidate for staff performance reviews. This application enhances the widely utilized TOPSIS approach within the context of fuzzy sets, showcasing the versatility and adaptability of the methodology.…”