Large-scale group decision-making based on Pythagorean linguistic preference relations using experts clustering and consensus measure with non-cooperative behavior analysis of clusters

Abstract

To represent qualitative aspect of uncertainty and imprecise information, linguistic preference relation (LPR) is a powerful tool for experts expressing their opinions in group decision-making (GDM) according to linguistic variables (LVs). Since for an LV, it generally means that membership degree is one, and non-membership and hesitation degrees of the experts cannot be expressed. Pythagorean linguistic numbers/values (PLNs/PLVs) are novel choice to address this issue. The aim of this paper which we propose a GDM problem involved a large number of the experts is called large-scale GDM (LSGDM) based on Pythagorean linguistic preference relation (PLPR) with a consensus model. Sometimes, the experts do not modify their opinions to achieve consensus. Therefore, the experts’ proper opinions’ management with their non-cooperative behaviors (NCBs) is necessary to establish a consensus model. At the same time, it is essential to ensure the proper adjustment of the credibility information. The proposed model using grey clustering method is divided with the experts’ similar evaluations into a subgroup. Then, we aggregate the experts’ evaluations in each cluster. A cluster consensus index (CCI) and a group consensus index (GCI) are presented to measure consensus level among the clusters. Then, we provide a mechanism for managing the NCBs of the clusters, which contain two parts: (1) NCB degree is defined using CCI and GCI for identifying the NCBs of the clusters; (2) implemented the weight punishment mechanism of the NCBs clusters to consensus improvement. Finally, an example is offered for usefulness of the proposed approach.

A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making

“…(3) Existing researches on clustering only rely on preference similarity or trust relationships ( Ma et al, 2019 ; Pan et al, 2020 ; Zhong & Xu, 2020 ; Sahu et al, 2020 ; Zheng et al, 2021 ; Mandal, Samanta & Pal, 2022 ). Otherwise, in practical problems, preference and trust play different roles in the clustering process.…”

Decision-Making Conflict Measurement of Old Neighborhoods Renovation Based on Mixed Integer Programming DEA-Discriminant Analysis (MIP DEA–DA) Models

“…Over time, the decision-making subject in the process of global democratization to diversification, so the composition of the group and decision-making mode has also changed; in the pluralistic and complex decision-making group structure, increasing numbers of scholars have conducted in-depth research on group decision-making. It is mainly embodied in decision-making expert clustering [34], decision attribute weighting [35], and consensusreaching processes [36], and the research focus is also mainly embodied in multi-attribute large-group decision-making methods [37], dynamic group decision-making methods [38], and multi-objective group decision-making methods [39]. Liu [40] examined the process of continuous improvement of the DEA-DA method, developed a more rigorous MIP DEA-DA clustering model for expert clustering, and laid the foundation for the subsequent quantification of the degree of conflict between clusters and enabling experts to reach consensus.…”

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

“…Step 1: Each experts or decision makers interacting each others using social network and then provide their performance with respect to either bipolar-valued fuzzy value [17][18][19][20] or fuzzy value [21].…”