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Decision process in MCDM with large number of criteria and heterogeneous risk preferences

Author

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  • Liu, Jian
  • Zhao, Hong-Kuan
  • Li, Zhao-Bin
  • Liu, Si-Feng

Abstract

A new decision process is proposed to address the challenge that a large number criteria in the multi-criteria decision making (MCDM) problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion) will be embedded in the decision process. Later, the optimal decision object is selected according to the risk preferences of decision makers based on the corresponding theoretic model. Finally, a new algorithm of information aggregation model is proposed based on fairness maximization of decision results for the group decision, considering the coexistence of decision makers with heterogeneous risk preferences. The scientific rationality verification of this new method is given through the analysis of real case.

Suggested Citation

  • Liu, Jian & Zhao, Hong-Kuan & Li, Zhao-Bin & Liu, Si-Feng, 2017. "Decision process in MCDM with large number of criteria and heterogeneous risk preferences," Operations Research Perspectives, Elsevier, vol. 4(C), pages 106-112.
    • Handle: RePEc:eee:oprepe:v:4:y:2017:i:c:p:106-112
    DOI: 10.1016/j.orp.2017.07.001
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    Cited by:

    1. Yazdi, Amir Karbassi & Wanke, Peter Fernandes & Hanne, Thomas & Abdi, Farshid & Sarfaraz, Amir Homayoun, 2022. "Supplier selection in the oil & gas industry: A comprehensive approach for Multi-Criteria Decision Analysis," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    2. Seyed Hossein Razavi Hajiagha & Jalil Heidary-Dahooie & Ieva Meidutė-Kavaliauskienė & Kannan Govindan, 2022. "A new dynamic multi-attribute decision making method based on Markov chain and linear assignment," Annals of Operations Research, Springer, vol. 315(1), pages 159-191, August.

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