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A q-rung orthopair hesitant fuzzy stochastic method based on regret theory with unknown weight information

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Abstract

The present study introduces a q-rung orthopair hesitant fuzzy stochastic method based on regret theory to capture the psychological behavior of decision makers (DMs) in decision making. For this, first, according to the score and variance function of q-rung orthopair hesitant fuzzy number (q-ROHFN), a novel group satisfaction degree is defined, which can precisely mirror the overall level and group divergence. And then, on this basis, an optimization model of criteria weights is constructed, and the Lagrange function is formulated to cope with the situation where the information about criteria weights is entirely unknown. Next, the regret value and the rejoice value are obtained by the provided regret-rejoice function, and the alternatives are ranked according to the total psychological perception values of DMs under multi-state situations. Lastly, a case study concerning stock investment is addressed to demonstrate the implantation of the provided method. Besides this, detailed sensitivity analysis and comparison with relevant literature are performed to discuss the stability and superiority of the presented method.

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Acknowledgements

The authors are grateful to the editor and anonymous reviewers for their insightful and valuable suggestions, which helped us to improve the presentation of this manuscript.

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Authors and Affiliations

  1. Department of Mathematics, Quaid-i-Azam University, Islamabad, 45320, Pakistan

    Jawad Ali & Zia Bashir

  2. Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, KPK, Pakistan

    Jawad Ali & Wali Khan Mashwani

  3. Department of Mathematics, University of Management and Technology, Lahore, 54770, Pakistan

    Tabasam Rashid

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  1. Jawad Ali
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  2. Zia Bashir
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  4. Wali Khan Mashwani
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Correspondence to Jawad Ali.

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Ali, J., Bashir, Z., Rashid, T. et al. A q-rung orthopair hesitant fuzzy stochastic method based on regret theory with unknown weight information. J Ambient Intell Human Comput 14, 11935–11952 (2023). https://doi.org/10.1007/s12652-022-03746-8

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