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Multiplicative Consistency Of Hesitant Fuzzy Preference Relation And Its Application In Group Decision Making

Author

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  • HUCHANG LIAO

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200052, China)

  • ZESHUI XU

    (Business School, Sichuan University, Chengdu 610064, China)

  • MEIMEI XIA

    (School of Economics and Management, Tsinghua University, Beijing 100084, China)

Abstract

As we may have a set of possible values when comparing alternatives (or criteria), the hesitant fuzzy preference relation becomes a suitable and powerful technique to deal with this case. This paper mainly focuses on the multiplicative consistency of the hesitant fuzzy preference relation. First of all, we explore some properties of the hesitant fuzzy preference relation and develop some new aggregation operators. Then we introduce the concepts of multiplicative consistency, perfect multiplicative consistency and acceptable multiplicative consistency for a hesitant fuzzy preference relation, based on which, two algorithms are given to improve the inconsistency level of a hesitant fuzzy preference relation. Furthermore, the consensus of group decision making is studied based on the hesitant fuzzy preference relations. Finally, several illustrative examples are given to demonstrate the practicality of our algorithms.

Suggested Citation

  • Huchang Liao & Zeshui Xu & Meimei Xia, 2014. "Multiplicative Consistency Of Hesitant Fuzzy Preference Relation And Its Application In Group Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 47-76.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:01:n:s0219622014500035
    DOI: 10.1142/S0219622014500035
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    Cited by:

    1. Hüseyin Akay, 2022. "Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6205-6222, December.
    2. Lihong Wang & Zaiwu Gong, 2017. "Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment," IJERPH, MDPI, vol. 14(10), pages 1-16, October.
    3. Mousavi, M. & Gitinavard, H. & Mousavi, S.M., 2017. "A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 774-787.
    4. Wanying Xie & Zeshui Xu & Zhiliang Ren & Hai Wang, 2018. "Probabilistic Linguistic Analytic Hierarchy Process and Its Application on the Performance Assessment of Xiongan New Area," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1693-1724, November.
    5. Xiaolin Xun & Yongbo Yuan, 2020. "Research on the urban resilience evaluation with hybrid multiple attribute TOPSIS method: an example in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 557-577, August.
    6. Akshay Hinduja & Manju Pandey, 2019. "A Distance-based Method for Computing Priorities of Intuitionistic Fuzzy Preference Relation and Its Application in AHP," Vision, , vol. 23(4), pages 329-340, December.
    7. Xiaoyan Zhang & Qian Yang, 2022. "A Novel Method for Decision Making by Double-Quantitative Rough Sets in Hesitant Fuzzy Systems," Mathematics, MDPI, vol. 10(12), pages 1-24, June.
    8. Qasim Noor & Tabasam Rashid & Syed Muhammad Husnine, 2021. "An extended TDM method under probabilistic interval-valued hesitant fuzzy environment for stock selection," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.

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