Cognitive fuzzy preference relations and its applications in decision-making

摘要

Cognitive fuzzy set reveals the reason why the sum of membership and non-membership degrees of an element to a set is larger than 1, and it defines the joint degree to represent the joint part of membership and non-membership degrees. Given that the cognitive fuzzy set can reflect cognitive overlaps of experts, this paper introduces the cognitive fuzzy preference relation (CFPR) to represent preference intensities of alternatives through pairwise comparisons. To facilitate the analyses with CFPRs, the operations of CFPRs are studied based on the t-norm and t-conorm. Afterwards, the entropy and cross-entropy of CFPRs are introduced and then used to determine the weights of criteria. The score function of a CFPR is proposed and an entropy-weight-based ranking method with CFPRs is introduced to rank alternatives. A case study on selecting agricultural food supply chains is given to demonstrate the applicability of the proposed method. Sensitivity analyses and comparative analyses demonstrate that the proposed method is reliable.