Multi-criteria decision-making based on bi-parametric exponential fuzzy information measures and weighted correlation coefficients

This paper proposes a new bi-parametric exponential fuzzy information measure. In addition to the validation of proposed fuzzy information measure, some of its major properties are also studied. Besides, the performance of proposed fuzzy information measure is demonstrated using two numerical examples. Further, based on the concept of TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method, a new improved TOPSIS method based on weighted correlation coefficients has been introduced. Considering the importance of criteria weights in the solution of Multi-Criteria Decision-Making (MCDM) problems, two methods have been discussed for the evaluation of criteria weights. In first method, criteria weight evaluation from the partial information provided by experts is discussed. Second method proposes the criteria weight evaluation in case they are completely unknown or incompletely known. The proposed MCDM method is explained through a numerical example based on fault detection in an ill-functioning machine.