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.
Highlights
The decision data provided by a huge number of decisionmakers (DMs) or the experts are known as large-scale group decision-making (LSGDM) problem, which is a widespread human activity for the selection of the best option from a set
Mandal et al [38] proposed the new type of preference relation called Pythagorean linguistic preference relation (PLPR), which is addressed the preferred degree and non-preferred degree of linguistic variables (LVs) according to Yagers Pythagorean fuzzy sets (PFSs) [53]
We provide the following definition to find the group consensus index (GCI) of the LSGDM based on PLPR
Summary
The decision data provided by a huge number of decisionmakers (DMs) or the experts are known as large-scale group decision-making (LSGDM) problem, which is a widespread human activity for the selection of the best option from a setB Prasenjit MandalComplex & Intelligent Systems titative judgment. Wang and Li [44] pointed out that the membership degree of a linguistic assessment value is one; the non-membership and hesitation degrees of DMs cannot be expressed. If a DM compares two alternatives at a time and gives the opinion according to an LV such as “good”, but he/she cannot be entirely sure that this assessment results. He/she has 75% certain degree and 8% degree of confusion. In this situation, Mandal et al [38] proposed the new type of preference relation called Pythagorean linguistic preference relation (PLPR), which is addressed the preferred degree and non-preferred degree of LVs according to Yagers Pythagorean fuzzy sets (PFSs) [53]. We suggest for interested researchers to see the attractive studies in [4,5,9,34,37]
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