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    A Self-Management Mechanism to Manage Non-cooperative Behaviors in LGDM-Based Supply Chain Risk Mitigation

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    Final accepted paper (311.7Kb)
    Date
    2019-01-17
    Author
    Zhao, Sihai;
    Dong, Yucheng;
    Zhang, Hengjie;
    Chiclana, Francisco;
    Herrera-Viedma, Enrique
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    Abstract
    Large-scale group decision making (LGDM) is becoming more and more common, and how to assure the security and quality of the decision making process has become a hot topic. Supply chain risk mitigation is a complex LGDM problem involving in many stakeholders. In the decision making process, a group of experts aims at reaching a consensus among alternatives in which non-cooperative behaviors often appear. Some experts might designedly form a small alliance and change their preferences in a direction against consensus with the aim to foster the alliance’s own interests. In this study, we present a novel large-scale consensus reaching framework based on a self- management mechanism to manage non-cooperative behaviors. In the proposed framework, experts are classified into different subgroups using a clustering method, and they provide their evaluation information, i.e., the multi-criteria mutual evaluation matrices (MCMEMs), regarding the obtained subgroups based on their performance. The subgroups’ weights are generated dynamically from the MCMEMs, which are in turn used to update experts’ weights. This mechanism allows penalizing the weights of the experts with non-cooperative behaviors. Detailed comparison analysis is presented to verify the validity of the proposed consensus framework for supply chain risk mitigation.
    Description
    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
    Citation : Zhao, S., Dong, Y., Zhang, H., Chiclana, F., Herrera-Viedma, E. (2018) A Self-Management Mechanism to Manage Non-cooperative Behaviors in LGDM-Based Supply Chain Risk Mitigation. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, article. no. 8616557, pp. 3307-3312.
    URI
    http://hdl.handle.net/2086/17623
    DOI
    https://doi.org/10.1109/SMC.2018.00560
    ISBN : 9781538666500
    ISSN : 2577-1655
    Research Group : Institute of Artificial Intelligence (IAI)
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2970]

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