Abstract
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.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.