A comparative analysis of objective weighting methods with intuitionistic fuzzy entropy measures

Instead of the traditional entropy measures which focus on the discrimination of attributes, we utilize the nature of intuitionistic fuzzy (IF) entropy measures which assess the weight of attributes based on the credibility of data to propose a new objective weighting method for solving the multiple attribute decision making (MADM) problems. In this proposed method, we executed various and the newest IF entropy measures introduced by Vlachos and Sergiadis [18], and Zeng and Li [25]. Both of them estimated the IF entropy from the viewpoint of membership and non-membership degree of intuitionistic fuzzy sets. A comparative analysis of experimental simulation which contains not only different combinations of given number of attributes and alternatives but also different IF entropy measures is designed to observe and discuss the outcomes. The experimental results indicated that different IF entropy measures applied in the weighting method would generate distinct weight values and the ranking of attributes even though the measures originated from the same theorem. Especially, the number of attributes decides the extent of similarity among IF entropy measures. With the new objective weighting method, the decision maker can combine it with his/her subjective weights to obtain a compromise attribute weights in MADM problems.