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Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Author

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  • Chao Sun

    (Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China
    School of information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Shiying Li

    (Glasgow College, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Yong Deng

    (Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

Multi-criteria decision making (MCDM) refers to the decision making in the limited or infinite set of conflicting schemes. At present, the general method is to obtain the weight coefficients of each scheme based on different criteria through the expert questionnaire survey, and then use the Dempster–Shafer Evidence Theory (D-S theory) to model all schemes into a complete identification framework to generate the corresponding basic probability assignment (BPA). The scheme with the highest belief value is then chosen. In the above process, using different methods to determine the weight coefficient will have different effects on the final selection of alternatives. To reduce the uncertainty caused by subjectively determining the weight coefficients of different criteria and further improve the level of multi-criteria decision-making, this paper combines negation of probability distribution with evidence theory and proposes a weights-determining method in MCDM based on negation of probability distribution. Through the quantitative evaluation of the fuzzy degree of the criterion, the uncertainty caused by human subjective factors is reduced, and the subjective error is corrected to a certain extent.

Suggested Citation

  • Chao Sun & Shiying Li & Yong Deng, 2020. "Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment," Mathematics, MDPI, vol. 8(2), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:191-:d:316592
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    References listed on IDEAS

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    Cited by:

    1. József Dombi & Tamás Jónás, 2024. "Learning the weights using attribute order information for multi-criteria decision making tasks," OPSEARCH, Springer;Operational Research Society of India, vol. 61(4), pages 2379-2409, December.

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