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.