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Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

Author

Listed:
  • Feifei Jin

    (Anhui University)

  • Jinpei Liu

    (Anhui University)

  • Ligang Zhou

    (Anhui University)

  • Luis Martínez

    (University of Jaén)

Abstract

Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs’ regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs’ regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

Suggested Citation

  • Feifei Jin & Jinpei Liu & Ligang Zhou & Luis Martínez, 2021. "Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory," Group Decision and Negotiation, Springer, vol. 30(4), pages 813-845, August.
    • Handle: RePEc:spr:grdene:v:30:y:2021:i:4:d:10.1007_s10726-021-09736-z
    DOI: 10.1007/s10726-021-09736-z
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    References listed on IDEAS

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    1. Pei Wang & Xuanhua Xu & Shuai Huang, 2019. "An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information," Group Decision and Negotiation, Springer, vol. 28(3), pages 619-640, June.
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    9. Feifei Jin & Zhiwei Ni & Reza Langari & Huayou Chen, 2020. "Consistency Improvement-Driven Decision-Making Methods with Probabilistic Multiplicative Preference Relations," Group Decision and Negotiation, Springer, vol. 29(2), pages 371-397, April.
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    Cited by:

    1. Feifei Jin & Chang Li & Jinpei Liu & Ligang Zhou, 2021. "Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis," Mathematics, MDPI, vol. 9(19), pages 1-19, October.
    2. Shaw, Lipika & Das, Soumen Kumar & Roy, Sankar Kumar, 2022. "Location-allocation problem for resource distribution under uncertainty in disaster relief operations," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    3. Tong, Huagang & Zhu, Jianjun, 2023. "A parallel approach with the strategy-proof mechanism for large-scale group decision making: An application in industrial internet," European Journal of Operational Research, Elsevier, vol. 311(1), pages 173-195.
    4. Lu Chen & Ayad Hendalianpour & Mohammad Reza Feylizadeh & Haiyan Xu, 2023. "Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL," Group Decision and Negotiation, Springer, vol. 32(2), pages 359-394, April.
    5. Fangqing Wei & Yanan Fu & Feng Yang & Chun Sun & Sheng Ang, 2023. "Closest target setting with minimum improvement costs considering demand and resource mismatches," Operational Research, Springer, vol. 23(3), pages 1-29, September.

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