Hierarchical Sequential Three-Way Multiattribute Decision-Making Method Based on Regret Theory in Multiscale Fuzzy Decision Systems | IEEE Journals & Magazine | IEEE Xplore

Hierarchical Sequential Three-Way Multiattribute Decision-Making Method Based on Regret Theory in Multiscale Fuzzy Decision Systems


Abstract:

Most of the existing multiattribute decision-making models under multiscale decision information systems are established by selecting the optimal scale or fusing multisca...Show More

Abstract:

Most of the existing multiattribute decision-making models under multiscale decision information systems are established by selecting the optimal scale or fusing multiscale information into a single scale. These models will lose part of the decision information, resulting in inaccurate decision results. However, sequential three-way decision can not only process information hierarchically, but also provide delayed decision between acceptance and rejection. In addition, the irrational behavior of decision-makers will have a certain impact on the decision-making results. To this end, for multiscale and diversity decision-making problems, this article proposes a hierarchical sequential three-way multiattribute decision-making method based on regret theory. Specifically, to represent this diversity, the multiscale evaluation information table is converted into a digital evaluation value table through a fuzzy membership function. Second, based on the regret-rejoicing function of regret theory, the regret-rejoicing relation of alternatives in multiscale information systems is established, which can be used to calculate the conditional probability. Third, the relative loss functions based on regret theory are proposed by considering the psychological behaviors of decision-makers. Finally, the hierarchical sequential three-way multiattribute decision-making method for solving the multiscale decision-making problem is proposed. The stability and effectiveness of the proposed method are verified by the corresponding experiments and the comparative analysis of practical cases. The proposed method solves the fusion problem of multiscale decision information and obtains flexible ranking results according to the risk factor.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 32, Issue: 9, September 2024)
Page(s): 4961 - 4975
Date of Publication: 07 May 2024

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