A consensus measure for group decision making with hesitant linguistic preference information based on double alpha-cut

The hesitant fuzzy linguistic term set, which is characterized by a set of possible linguistic terms, becomes a useful tool to describe the complex cognitions of experts. In many cases, it is much more flexible for experts to provide evaluation information via pairwise comparisons rather than give their evaluation information on options directly. In this regard, the hesitant fuzzy linguistic preference relation (HFLPR) whose elements are hesitant fuzzy linguistic elements was introduced. Besides, to make a reasonable decision, a group of experts are usually involved in practice and thus how to reach an agreement among experts whose perceptions are represented by HFLPRs turns to be significantly important. To meet this challenge, in this study, we propose a novel consensus measure for group decision making with HFLPRs. Firstly, we define an alpha-cut-based method to calculate the similarities among different hesitant fuzzy linguistic evaluations and then develop an alpha-cut-based consensus measure for group decision making with HFLPRs. By converting the linguistic terms of hesitant fuzzy linguistic elements into numerical values of 0 or 1, the consensus measure can be simplified. Finally, to illustrate the effectiveness of the method, we apply it to a practical case study. A comparative analysis is provided to demonstrate the advantages and significance in practical applications of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The hesitant fuzzy linguistic term set is useful in describing complex expert cognitions, experts provide evaluation information through pairwise comparisons, and a consensus measure simplifies numerical conversion of hesitant fuzzy linguistic elements.