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Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization

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Abstract

Consensus reaching processes (CRPs) including the feedback adjustment mechanism generally require extended periods of time to bridge the opinion gap among decision makers. Therefore, minimum cost consensus (MCC) problems with known adjustment costs have been widely reported. However, the exact unit adjustment costs are difficult to obtain through practical CRPs. To solve these problems, this paper proposes a novel CRP framework for uncertain large-scale group decision-making based on robust discrete optimization. First, an enhanced iterative self-organizing data analysis technique algorithm is provided to dynamically cluster decision makers together in small subgroups under interval opinions. Second, to establish the optimization-based consensus rules in the feedback process, an MCC integer optimization model is established to minimize the total consensus costs in consensus reaching. Furthermore, with the indeterminate unit adjusting costs, a robust discrete MCC optimization model is constructed, which can control the degree of conservatism of the optimal consensus opinion and compute the optimal modified opinions of decision makers. Finally, a case study and comparative analysis indicate the effectiveness and superiority of the proposed CRP method and that the robust discrete MCC model has stronger robustness in the uncertain decision environment.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions. This research was supported by the Philosophy and Social Science of Shanghai (NO. 2020BGL010). This research was also supported by the National Social Science Foundation (No. 21ZDA105) and the National Natural Science (No. 72171123, 72171149).

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Authors and Affiliations

  1. University of Shanghai for Science and Technology, Shanghai, China

    Yuanming Li

  2. School of Management, Shanghai University, Shanghai, China

    Ying Ji

  3. Nanjing University of Information Science and Technology, Nanjing, China

    Shaojian Qu

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  1. Yuanming Li
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  2. Ying Ji
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Correspondence to Ying Ji.

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Li, Y., Ji, Y. & Qu, S. Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization. Group Decis Negot 31, 453–489 (2022). https://doi.org/10.1007/s10726-022-09774-1

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