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DOI number:10.1016/j.cie.2022.108818
Journal:Computers & Industrial Engineering（中科院一区，FMS B类）
Key Words:Scenario-based social network analysis；Dominance principle；Parallel dynamic feedback；Consensus reaching process
Abstract:With the scale of group decision making increasing, it is a crucial issue to get the utmost of collective intelligence for seeking the optimal solution. In this study, we propose an automatic consensus reaching process (CRP) for large-scale group decision making (LSGDM) based on parallel dynamic feedback strategy and two-dimensional scenario-based social network analysis (SNA) model. Firstly, individuals express their preferences by distributed preference relations (DPRs) which could keep the uncertainty of assessment and allow multi-attribute comparison. Secondly, SNA based on trust relationship and connection strength is implemented. Then a twodimensional scenario-based SNA model is established, and a fuzzy clustering algorithm based on connection strength is designed to reduce the scale of decision makers (DMs). Finally, a two-phase CRP with identification rules and feedback strategy is constructed. Identification rules are used for activating different kinds of feedback mechanisms by identifying whether it reaches acceptable local or global consensus. The rules also identify which kind of social relationship for internal or external subgroups and what dominance does individual or subgroup has. Feedback strategy with parallel dynamic adjustment process is further designed based on opinion and trust adjustment factors and non-cooperative behaviors. A real illustrative case for selecting the optimal carbon footprint management provider is presented to demonstrate the validity of our proposed method, and further compare it with other current methods.
Co-author:Mi Zhou(通讯作者),Xin-Bao Liu,Ba-Yi Cheng,Enrique Herrera-Viedma
First Author:Ya-Jing Zhou
Indexed by:Journal paper
Document Code:108818
Discipline:Management Science
Document Type:J
Volume:174
Page Number:108818
Translation or Not:no
Date of Publication:2022-12-10
Included Journals:SCI