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Novel Multi-criteria Decision-making Approaches Based on Hesitant Fuzzy Sets and Prospect Theory

Author

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  • Juan-Juan Peng

    (School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China†School of Business, Central South University, Changsha, 410083, China)

  • Jian-Qiang Wang

    (#x2020;School of Business, Central South University, Changsha, 410083, China)

  • Xiao-Hui Wu

    (School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China†School of Business, Central South University, Changsha, 410083, China)

Abstract

Hesitant fuzzy sets (HFSs), an extension of fuzzy sets, are considered to be useful in solving decision making problems where decision makers are unable to choose between several values when expressing their preferences. The purpose of this paper is to develop two hesitant fuzzy multi-criteria decision making (MCDM) methods based on prospect theory (PT). First, the novel component-wise ordering method for two hesitant fuzzy numbers (HFNs) is defined; however, this method does not consider the length of the two HFNs. Second, by utilizing the directed Hausdorff distance between two imprecise point sets, the generalized hesitant Hausdorff distance is developed, which overcomes the shortcomings of the existing distance measures. Third, based on the proposed comparison method and distance, as well as PT, the extended TODIM and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) approaches are developed in order to solve MCDM problems with hesitant fuzzy information. Finally, a practical example is provided to illustrate the pragmatism and effectiveness of the proposed approaches. Sensitivity and comparison analyses are also conducted using the same example. The findings indicate that the proposed methods do not require complicated computation procedures, yet still yield a reasonable and credible solution.

Suggested Citation

  • Juan-Juan Peng & Jian-Qiang Wang & Xiao-Hui Wu, 2016. "Novel Multi-criteria Decision-making Approaches Based on Hesitant Fuzzy Sets and Prospect Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 621-643, May.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:03:n:s0219622016500152
    DOI: 10.1142/S0219622016500152
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    Cited by:

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    2. Xiaodi Liu & Zengwen Wang & Antoinette Hetzler, 2017. "HFMADM method based on nondimensionalization and its application in the evaluation of inclusive growth," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(4), pages 726-744, July.
    3. Mustafa Batuhan Ayhan, 2018. "A New Decision Making Approach for Supplier Selection: Hesitant Fuzzy Axiomatic Design," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1085-1117, July.
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    5. Tosarkani, Babak Mohamadpour & Amin, Saman Hassanzadeh & Zolfagharinia, Hossein, 2020. "A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network," International Journal of Production Economics, Elsevier, vol. 224(C).
    6. Ying Luo & Xudong Chen & Liming Yao, 2021. "Flood disaster resilience evaluation of Chinese regions: integrating the hesitant fuzzy linguistic term sets with prospect theory," 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. 105(1), pages 667-690, January.
    7. Jing Wang & Jian-Qiang Wang & Hong-Yu Zhang & Xiao-Hong Chen, 2017. "Distance-Based Multi-Criteria Group Decision-Making Approaches with Multi-Hesitant Fuzzy Linguistic Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1069-1099, July.
    8. Juan-juan Peng & Jian-qiang Wang & Wu-E Yang, 2017. "A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(2), pages 425-435, January.

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