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A comparative analysis of probabilistic linguistic preference relations and distributed preference relations for decision making

Author

Listed:
  • Min Xue

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies)

  • Chao Fu

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies)

  • Shanlin Yang

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies)

Abstract

When a decision-maker prefers to compare different alternatives in pairs to handle real situations, there are many different expression styles that can be used. Two representative expression styles are the probabilistic linguistic preference relation (PLPR), which originates from the fuzzy linguistic approach and the distributed preference relation (DPR), which originates from the evidential reasoning approach. Although these two expression styles look quite similar, their meanings, operations, and relevant decision making processes are significantly different. This presents the decision-maker with the challenge of selecting either PLPRs or DPRs in different real cases. To address this issue, this paper provides a detailed analysis of the similarities and differences between PLPRs and DPRs. The analysis is conducted from five perspectives, including modeling of decision making problems, handling of uncertainty, consistency between preference relations, information aggregation, and elicitation process. An engineer selection problem for an automobile manufacturing enterprise is investigated to demonstrate how to appropriately select PLPRs or DPRs to model and analyze decision making problems in real situations with consideration for the preferences of decision-makers.

Suggested Citation

  • Min Xue & Chao Fu & Shanlin Yang, 2022. "A comparative analysis of probabilistic linguistic preference relations and distributed preference relations for decision making," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 71-97, March.
    • Handle: RePEc:spr:fuzodm:v:21:y:2022:i:1:d:10.1007_s10700-021-09357-w
    DOI: 10.1007/s10700-021-09357-w
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    References listed on IDEAS

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    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    2. Hong-gang Peng & Jian-qiang Wang, 2020. "Multi-criteria sorting decision making based on dominance and opposition relations with probabilistic linguistic information," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 435-470, December.
    3. Xu, Zeshui, 2005. "Deviation measures of linguistic preference relations in group decision making," Omega, Elsevier, vol. 33(3), pages 249-254, June.
    4. Huchang Liao & Xiaomei Mi & Zeshui Xu, 2020. "A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 81-134, March.
    5. Herrera, F. & Herrera-Viedma, E. & Chiclana, F., 2001. "Multiperson decision-making based on multiplicative preference relations," European Journal of Operational Research, Elsevier, vol. 129(2), pages 372-385, March.
    6. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    7. Pei Liang & Junhua Hu & Bo Li & Yongmei Liu & Xiaohong Chen, 2020. "A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 499-528, December.
    8. Fu, Chao & Chang, Wenjun & Xue, Min & Yang, Shanlin, 2019. "Multiple criteria group decision making with belief distributions and distributed preference relations," European Journal of Operational Research, Elsevier, vol. 273(2), pages 623-633.
    9. Chao Fu & Dong-Ling Xu & Shan-Lin Yang, 2016. "Distributed preference relations for multiple attribute decision analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 457-473, March.
    10. Na Yue & Dongrui Wu & Jialiang Xie & Shuili Chen, 2020. "Probabilistic linguistic multi-criteria decision-making based on double information under imperfect conditions," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 391-433, December.
    11. Liao, Huchang & Wu, Xingli & Mi, Xiaomei & Herrera, Francisco, 2020. "An integrated method for cognitive complex multiple experts multiple criteria decision making based on ELECTRE III with weighted Borda rule," Omega, Elsevier, vol. 93(C).
    Full references (including those not matched with items on IDEAS)

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