Optimal consensus models for group decision making under linguistic preference relations

Abstract

This paper proposes an optimal consensus model to derive weights for linguistic preference relations (LPRs). Two indexes, an individual‐to‐group consensus index (ICI) and a collective consensus index (CCI), are introduced. An iterative algorithm is presented to describe the consensus reaching process. By changing the weights and modifying a pair of individuals' comparison judgments—which have largest deviation value to the group judgments—the consensus reaching process can terminate, while both ICI and CCI are controlled with predefined thresholds. The algorithm aims to preserve the decision makers’ original information as much as possible. The model and algorithm are then extended to handle the uncertain additive LPRs. Finally, two examples are given to show the effectiveness of the proposed methods.

Multicriteria decision‐making approach based on gray linguistic weighted Bonferroni mean operator

“…Although FSs and IFSs have been developed and generalized, they cannot deal with all sorts of fuzziness in real decision‐making problems, such as problems that are too complex or ill‐defined to be solved by quantitative expressions. Zadeh () introduced the linguistic variable, which is an effective tool because using linguistic information can enhance the reliability and flexibility of classical decision models (Yang and Wang, ; Merigó et al., ; Yang, ; Xu et al., ). Recently, linguistic variables have been studied in depth and numerous MCDM methods integrated with other theories have been developed.…”

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

“…Considering this situation, GDM is proposed. GDM with PRs has been deeply studied by researchers, where the group consensus is a hot researching topic (Li et al, ; Meng, An, & Chen, ; Massanet et al, ; Wu et al, ; Xu, Sun, & Wang, ; Zhao et al, ; Zhang & Guo, ). In consideration of GDM with HFLPRs, this section first introduces a new distance measure between HFLPRs.…”

Multiplicative consistency analysis of linguistic preference relation with self‐confidence level and self‐doubting level and its application in a group decision making

“…There have been many developments on the GDM problems with LPRs up to date. The structural architecture for the GDM using LPRs is normally composed of three modules from current accomplishments: (1) consistency monitoring and enhancements, [17][18][19] (2) a consensus reaching process (CRP), [20][21][22][23][24] and (3) a priority weight for alternative solutions. 4 While the above three modules have given significant GDM contributions using LPRs, there are still certain limitations, defined as follows:…”