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A group consensus reaching model balancing individual satisfaction and group fairness for distributed linguistic preference relations

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Abstract

In real-world complex group decision-making problems, preference inconsistency and opinion conflict are common and crucial challenges that need to be tackled. To promote consensus reaching, a novel group consensus reaching model is constructed considering individual satisfaction and group fairness. This study focuses on managing the group consensus reaching process based on flexible and adaptable information, modelled as distributed linguistic preference relations (DLPRs). First, a building process for DLPRs is discussed by integrating cumulative distribution functions converted from single linguistic term sets, hesitant fuzzy linguistic term sets, and comparative linguistic expressions. Furthermore, a two-stage consistency improvement method is proposed, which makes a trade-off between the frequency and magnitude of adjustments. Finally, we establish an improved group consensus model to balance individual satisfaction and group fairness, where individual satisfaction is measured by the deviation between the original and revised preferences and group fairness is measured by the deviation between individual satisfactions. The emergency response plan selection is conducted to show the validity and advantages of the proposed approach.

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Acknowledgements

The work is Funded by Science Research Project of Hebei Education Department (BJK2024196) and the Humanities and Social Science Fund of Ministry of Education of China (Grant No.24YJC630124).

Funding

Science Research Project of Hebei Education Department, BJK2024196, Yinging Liang The Humanities and Social Science Fund of Ministry of Education of China, 24YJC630124, Yinging Liang.

Author information

Authors and Affiliations

  1. School of Economics and Management, Hebei University of Technology, Tianjin, 300401, China

    Yingying Liang

  2. School of Economics, Beijing Institute of Technology, Beijing, 100081, China

    Tianyu Zhang

  3. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, Hubei, China

    Yan Tu

  4. Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy

    Qian Zhao

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  1. Yingying Liang
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Correspondence to Yan Tu.

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Appendix

Appendix

DLPRs based matrices are provided by DMs in SubSect. 4.1 as follows:

DLPRs based matrices are converted into FPRs based matrices in SubSect. 4.2 as follows:

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Liang, Y., Zhang, T., Tu, Y. et al. A group consensus reaching model balancing individual satisfaction and group fairness for distributed linguistic preference relations. Appl Intell 54, 12697–12724 (2024). https://doi.org/10.1007/s10489-024-05732-3

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