Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making

The fuzzy preference relation with self-confidence (FPR-SC), whose elements are composed of the degree to which an alternative is preferred to another and the self-confidence level about the preference degree, is a useful tool for decision makers to express their preference information over alternatives. In this paper, an extended logarithmic least squares method (LLSM) is first proposed to derive a priority weight vector from an FPR-SC, based on which the multiplicative consistency of an FPR-SC is further defined and two algorithms are devised to improve the multiplicative consistency of an unacceptably consistent FPR-SC. Furthermore, we develop a novel approach to two-sided matching decision making with FPRs-SC based on the LLSM and the proposed consistency improving algorithms. Eventually, the feasibility and effectiveness of the two-sided matching decision making approach are demonstrated by an example for the matching of knowledge suppliers and knowledge demanders.