Analysis of self‐confidence indices‐based additive consistency for fuzzy preference relations with self‐confidence and its application in group decision making. Issue 5 (18th December 2018)

• Record Type: Journal Article
• Authors: Liu, Xia; Xu, Yejun; Montes, Rosana; Dong, Yucheng; Herrera, Francisco
• Is Part Of: International journal of intelligent systems. Volume 34:Issue 5(2019)
• Page Start: 920
• Page End: 946
• Publication Date: 2018-12-18
• Subjects: additive consistency -- fuzzy preference relations with self‐confidence (FPRs‐SC) -- induced ordered weighted averaging (IOWA) operator -- self‐confidence levels; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22081
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Preference relations have been widely used in group decision‐making (GDM) problems. Recently, a new kind of preference relations called fuzzy preference relations with self‐confidence (FPRs‐SC) has been introduced, which allow experts to express multiple self‐confidence levels when providing their preferences. This paper focuses on the analysis of additive consistency for FPRs‐SC and its application in GDM problems. To do that, some operational laws for FPRs‐SC are proposed. Subsequently, an additive consistency index that considers both the fuzzy preference values and self‐confidence is presented to measure the consistency level of an FPR‐SC. Moreover, an iterative algorithm that adjusts both the fuzzy preference values and self‐confidence levels is proposed to repair the inconsistency of FPRs‐SC. When an acceptable additive consistency level for FPRs‐SC is achieved, the collective FPR‐SC can be computed. We aggregate the individual FPRs‐SC using a self‐confidence indices‐based induced ordered weighted averaging operator. The inherent rule for aggregation is to give more importance to the most self‐confident experts. In addition, a self‐confidence score function for FPRs‐SC is designed to obtain the best alternative in GDM with FPRs‐SC. Finally, the feasibility and validity of the research are demonstrated with an illustrative example and some comparative analyses.