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学者姓名：钟香玉
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Abstract ：
As an extension of group decision-making in terms of scale and uncertainty, linguistic Z-number large-scale decision-making (LZ-LSDM) is emerging as a prominent research topic in the field of decision science. The unique structure of LZ-LSDM presents new challenges for both clustering analysis and consensus building. Minimum-cost consensus (MCC) based on the optimization principle is widely recognized as an effective tool for managing the consensus-reaching process. However, there is a scarcity of literature that addresses the study of MCC within the context of LZ-LSDM, as well as the application of MCC in the identification and treatment of non-cooperative behaviors. To this end, this study proposes a punishment strategy-driven multi-stage type-α constrained MCC model for LZ-LSDM problems. First, a similarity constraint-based clustering method with linguistic Z-numbers is proposed. Given the clustering results, a type-α constrained MCC (α-CMCC) model with personalized feedback constraints is designed to provide a personalized solution for visualizing opinion adjustment and preventing over-adjustment. Based on the optimal solution obtained by α-CMCC, the identification rule for non-cooperative behaviors is proposed. We conclude three punishment strategies—namely, pure, mixed, and cross—to address non-cooperative behaviors by arranging and combining commonly used punishment approaches. Finally, we illustrate the feasibility and validity of the model through a case study designed to facilitate consensus among an online patient community on knowledge-based recommendations. An exhaustive comparative analysis reveals the advantages and features of the proposed consensus model. © 2024 Elsevier Ltd
Keyword ：
Group knowledge recommendation consensus Group knowledge recommendation consensus Linguistic Z-number large-scale decision-making Linguistic Z-number large-scale decision-making Multi-stage type-α constrained minimum-cost consensus Multi-stage type-α constrained minimum-cost consensus Non-cooperative behavior Non-cooperative behavior Personalized feedback constraint Personalized feedback constraint Punishment strategy Punishment strategy
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| GB/T 7714 | Du, Z. , Yu, S. , Guo, L. et al. Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making [J]. | Engineering Applications of Artificial Intelligence , 2024 , 136 . |
| MLA | Du, Z. et al. "Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making" . | Engineering Applications of Artificial Intelligence 136 (2024) . |
| APA | Du, Z. , Yu, S. , Guo, L. , Zhong, X. . Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making . | Engineering Applications of Artificial Intelligence , 2024 , 136 . |
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Abstract ：
In the context of large group decision-making (LGDM), the opinions of individuals can influence each other due to their trust relationships. So, trust relationships should be deemed as just as important as evaluation information, and they should be considered jointly throughout the LGDM. This study first transforms the trust relationships between decision-makers into an information type, labeled as compromise information, whose form is the same as the evaluation information. The compromise information is utilized to incorporate trust relationships into various stages of the decision-making process, including clustering, weight determination, consensus reaching, and alternative selection. In the expert clustering and weight determination processes, more criteria and factors are considered by considering the compromise information. In the consensus reaching process, an optimization model is built to adjust the evaluation information of clusters to simultaneously guarantee a substantial increase in the global consensus level and minimize the adjustment cost. The compromise information also serves as a reference to limit the range of the adjusted information. An objective method to determine the consensus threshold is proposed. The proposed method is validated through an application example and comparisons, demonstrating its rationality and effectiveness. Simulation results indicate that the proposed consensus reaching method converges regardless of the number of experts, alternatives, and criteria. The proposed method integrates evaluation information and trust relationships into the LGDM process, thereby improving the rationality and scientificity of the decision results.
Keyword ：
Cluster weights Cluster weights Consensus reaching process Consensus reaching process Expert clustering Expert clustering Large group decision-making Large group decision-making Social network analysis Social network analysis
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| GB/T 7714 | Zhong, Xiangyu , Xu, Xuanhua , Goh, Mark et al. Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships [J]. | COGNITIVE COMPUTATION , 2023 , 16 (1) : 86-106 . |
| MLA | Zhong, Xiangyu et al. "Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships" . | COGNITIVE COMPUTATION 16 . 1 (2023) : 86-106 . |
| APA | Zhong, Xiangyu , Xu, Xuanhua , Goh, Mark , Pan, Bin . Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships . | COGNITIVE COMPUTATION , 2023 , 16 (1) , 86-106 . |
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