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The classification-based consensus in multi-attribute group decision-making

Author

Listed:
  • Xin Chen
  • Weijun Xu
  • Haiming Liang
  • Yucheng Dong

Abstract

In multi-attribute group decision-making problem (MAGDM), the existing consensus reaching process (CRP) is to obtain a consensus ranking of alternatives. However, these CRPs contradict some real-life MAGDM problems in which decision-makers do not need to rank alternatives and hope to classify the alternatives into several groups instead. Thus, in this paper we propose a new CRP in MAGDM, called the classification-based consensus reaching process (CCRP). First, we present a feedback method with minimum adjustments to generate the optimal adjusted individual matrices via a 0–1 mixed linear programming model for CCRP. Subsequently, we develop the interactive consensus reaching process based on the feedback method with minimum adjustments in CCRP. Finally, a practical example from China Undergraduate Mathematical Contest in Modeling and a simulation analysis are conducted to demonstrate the validity of the proposed CCRP.

Suggested Citation

  • Xin Chen & Weijun Xu & Haiming Liang & Yucheng Dong, 2020. "The classification-based consensus in multi-attribute group decision-making," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(9), pages 1375-1389, September.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:9:p:1375-1389
    DOI: 10.1080/01605682.2019.1609888
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    Cited by:

    1. Wenfeng Zhu & Hengjie Zhang & Jing Xiao, 2023. "Coming to Consensus on Classification in Flexible Linguistic Preference Relations: The Role of Personalized Individual Semantics," Group Decision and Negotiation, Springer, vol. 32(5), pages 1237-1271, October.
    2. Zhen Zhang & Zhuolin Li, 2023. "Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making," Annals of Operations Research, Springer, vol. 325(2), pages 911-938, June.

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