Approach for multi-attribute decision making based on novel intuitionistic fuzzy entropy and evidential reasoning

Abstract

A novel intuitionistic fuzzy entropy (IFE) is firstly proposed to overcome history studies shortcomings that neglect hesitancy degree and uncertainty degree. For intuitionistic fuzzy multi-attribute group decision-making problems with unknown or partially unknown weight information, a new decision-making method based on novel entropy and evidential reasoning is proposed. Firstly, an optimized weight solution model based on minimizing intuitionistic fuzzy entropy values is presented. Secondly, evidential reasoning methodology is utilized to aggregate the initial decision information to the final intuitionistic fuzzy values of alternatives instead of aggregation operators. Finally, we rank alternatives on the basis of similarity degrees which are calculated based on TOPSIS and Hamming distance. Case studies and comparison analysis are given to illustrate the effectiveness and superiority of the proposed model. Furthermore, the weight results of novel fuzzy entropy measure and several traditional fuzzy entropy measures are simulated and analyzed. It is concluded that the weights rely on not only entropy measures but also the numbers of alternatives and attributes. When the number of attributes increases, the discrepancy between the IFE measures increases.