A Two-Stage Minimum Cost Consensus Reaching Study for Large Scale Group Decision Making Based on the Opinion Leader–Follower Opinion Evolution

Two-stage consensus model based on opinion dynamics and evolution of social power in large-scale group decision making

Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations

Managing manipulative and non-cooperative behaviors in large scale group decision making based on a WeChat-like interaction network

Circular supply chain management with large scale group decision making in the big data era: The macro-micro model

An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information

Recently, large scale group decision making (LSGDM) problems have become a hotspot. This paper focuses on hesitant fuzzy LSGDM problems where decision makers (DMs) use hesitant fuzzy preference relations (HFPRs) to express their assessment information. HFPRs can represent the fuzziness and hesitancy of DM assessment information well. To improve the efficiency of hesitant fuzzy LSGDM problems, we first propose a reliability index-based consensus reaching process (RI-CRP). By assessing the ordinal consistency of DM's assessment information and measuring the deviation from collective opinion, the DM's opinion reliability index is given. To avoid unreliable information, we propose an unreliable DM management method to be used in the RI-CRP, based on the computation of DM's opinion reliability index. Moreover, an alternative ranking-based clustering (ARC) method with HFPRs is proposed to improve the efficiency of the RI-CRP. The similarity index between two DMs’ opinions is provided to ensure the ARC method can be effectively implemented. Compared with those clustering methods which need to preset several correlated parameters, the presented ARC method is more objective with a different approach based on the alternative ranking. A numerical example shows that the proposed ARC method and the RI-CRP are feasible and effective for hesitant fuzzy LSGDM problems.

A consensus model for large scale group decision making with hesitant fuzzy linguistic information and hierarchical feedback mechanism

During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research.

Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors

Product improvement in a big data environment: A novel method based on text mining and large group decision making

Study of the Game Model of E-Commerce Information Sharing in an Agricultural Product Supply Chain based on fuzzy big data and LSGDM

A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment

The disaster caused by landslide is huge. To prevent the spread of the disaster to the maximum extent, it is particularly important to carry out landslide disaster treatment work. The selection of landslide disaster treatment alternative is a large scale group decision-making (LSGDM) problem. Because of the wide application of social media, a large number of experts and the public can participate in decision-making process, which is conducive to improving the efficiency and correctness of decision-making. A IF-TW-LSGDM method based on three-way decision (TWD) and intuitionistic fuzzy set (IFS) is proposed and applied to the selection of landslide treatment alternatives. First of all, considering that experts and the public participate in the evaluation of LSGDM events, respectively, the method of obtaining and handling the public evaluation information is given, and the information fusion approach of the public and experts evaluation information is given. Second, evaluation values represented by fuzzy numbers are converted into intuitionistic fuzzy numbers (IFNs), and the intuitionistic fuzzy evaluation decision matrix described by IFNs is obtained. Then, a new LSGDM method of alternatives classification and ranking based on IFS and TWD is proposed, the calculation steps and algorithm description are given. In this process, we first cluster the experts, then consider the identification and management of non-cooperative behavior of expert groups. This work provides an effective method based on LSGDM for the selection of landslide treatment alternatives. Finally, the sensitivity of parameters is analyzed, and the feasibility and effectiveness of this method are compared and verified.

Consensus reaching processes (CRPs) in Group Decision Making (GDM) try to reach a mutual agreement among a group of decision makers before making a decision. To facilitate CRPs, multiple consensus models have been proposed in the literature. Classically, just a few decision makers participated in the CRP, however nowadays, the appearance of new technological environments and paradigms to make group decisions demand the management of larger scale problems that add new requirements to the solution of consensus. This contribution presents a study of a classical CRP applied to large-scale GDM in order to analyze its performance and detect which are the main challenges that these processes face in large-scale GDM. The analysis will be carried out in a java-based framework, AFRYCA 2.0, simulating different scenarios in large scale GDM.

A two-stage minimum adjustment consensus model for large scale decision making based on reliability modeled by two-dimension 2-tuple linguistic information