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

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.

Keywords:

• Fuzzy preference relation
• self-confidence
• consistency
• two-sided matching

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.

Additional information

Funding

This work was partly supported by the National Natural Science Foundation of China (NSFC) under Grant 71501023, Grant 71971039 and Grant 71771034, the Funds for Creative Research Groups of China under Grant 71421001, the Key Program of the NSFC under Grant 71731003, the Scientific and Technological Innovation Foundation of Dalian under Grant 2018J11CY009 and Grant 2018RQ69.