Journal
INFORMATION FUSION
Volume 80, Issue -, Pages 121-136Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2021.11.001
Keywords
Large-scale group decision making; Quadratic programming; Minimum cost consensus model; Consensus network evolution; Louvain algorithm
Funding
- National Natural Science Founda-tion of China (NSFC) [71701158, 72071151]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [17YJC630114]
- Fundamental Research Funds for the Central Universities [2019GYZX015, 2020GYZX003]
- Natural Science Foundation of Hubei Province [2020CFB773]
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The study proposes to combine social network analysis and minimum cost consensus models to form a complete decision-making system by introducing satisfaction index and consistency index for optimization. The Louvain algorithm is used to divide the group into subgroups to ensure independence and cohesion. The final decision ranking is based on the evaluation of group opinions in each subgroup.
Large-scale group decision-making problems based on social network analysis and minimum cost consensus models (MCCMs) have recently attracted considerable attention. However, few studies have combined them to form a complete decision-making system. Accordingly, we define the satisfaction index to optimize the classical MCCM by considering the effect of the group on individuals. Similarly, we define the consistency index to optimize the consensus reaching process (CRP). Regarding the evolution of the consensus network, the Louvain algorithm is used to divide the entire group into several subgroups to ensure that each subgroup is independent but has strong cohesion. By constructing the MCCM based on the satisfaction index and the optimized consensus-reaching process, the group opinions in each subgroup are ranked to obtain the final ranking of alternatives. Finally, to verify the validity of CRP and the practical value of the proposed model, we conduct consensus network evolution and decision-making analysis in the case of a negotiation between the government and polluting companies to achieve uniform pollution emissions. Sensitivity analysis is performed to demonstrate the stability of the subgroup weights. Furthermore, a comparative analysis using existing models verifies the effectiveness of the proposed model.
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