A consensus model for large scale group decision making with hesitant fuzzy linguistic information and hierarchical feedback mechanism. (November 2022)

• Record Type: Journal Article
• Authors: Li, Shengli; Rodríguez, Rosa M.; Wei, Cuiping; Shu, Ting
• Is Part Of: Computers & industrial engineering. Volume 173(2022)
• Publication Date: 2022-11
• Subjects: Consensus reaching process -- Possibility distributed -- Hesitant fuzzy linguistic term sets -- Hierarchical feedback mechanism -- Large scale group decision making; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108669
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: Large scale group decision making (LSGDM) has become a hot research topic in the field of decision science. Reaching a consensus in LSGDM is more difficult and complex than the traditional group decision making (GDM). In this paper, we propose a consensus model with hierarchical feedback mechanism for LSGDM dealing with hesitant fuzzy linguistic information. According to the majority rule, the sub-groups with more members are the ones playing a key role in the consensus reaching process. So the sub-groups are divided into three categories including dominant influencer, medium level of influencer and non-influencer. Then, to keep as much information as possible, we use possibility distributed extended hesitant fuzzy linguistic term sets to represent the sub-group opinion. After that, we take into account the sub-group size and the level of togetherness of the sub-group preferences to obtain the weight of each sub-group. Furthermore, a new hierarchical feedback mechanism is developed to provide different identification rules and recommendation strategies for the sub-groups performing different profiles. Finally, an application example of water resource management is utilized to show the effectiveness of the proposed model. Highlights: We propose a consensus model with hierarchical feedback mechanism. The sub-groups are divided into three categories. Different identification and recommendation rules are provided for different sub-groups. We use possibility distributed HFLTSs to model the sub-group hesitation.