Dynamic q-rung orthopair hesitant fuzzy decision-making model based on Banzhaf value of fuzzy measure

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-03-28 DOI:10.1016/j.asoc.2025.113036

Yibo Wang , Xiuqin Ma , Hongwu Qin , Yuanyuan Chen , Jemal H. Abawajy

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引用次数: 0

Abstract

Multi-attribute decision-making in dynamic fuzzy environments holds significant practical importance in fields. The changing fuzzy environmental factors and the hesitant psychology during the dynamic period commonly involve potential historical impacts of decisions and dynamic fuzzy intercorrelation. Quantitative indicators of attribute importance derived from fuzzy measures can account for intercorrelation but cannot ensure the dynamic feedback of historical decisions. For these reasons, the existing fuzzy decision-making methods have failure and poor stability when dealing with complex, hesitant dynamic fuzzy decision-making problems. Therefore, this paper aims to construct a dynamic decision-making model within the q-rung orthopair hesitant fuzzy(q-ROHF) environment. First, this paper derives the q-ROHF cross-entropy and four combinations of entropy formulas to derive the calculation method for the fuzzy measure of dynamic attributes. The Banzhaf value is used as a weighting metric to quantify the degree of impact of dynamic attributes to the decision result. Second, based on the Banzhaf value, a dynamic weighting algorithm incorporating a historical feedback mechanism and dynamic intercorrelation is proposed. Further, this paper derives a fuzzy preference relation(FPR) using the q-ROHF generalized fuzzy distance and presents an algorithm for updating the dynamic FPR matrix. Finally, a dynamic FPR-based AQM method is used to construct the dynamic decision-making model. The feasibility and effectiveness of the decision-making model in response to changing complex factors are demonstrated through two distinct real-world cases. Through robustness analysis and advantage analysis, it has been verified that the model exhibits superior dynamic decision-making stability and hesitant decision-making stability compared to existing models. The model can be a powerful tool for dealing with dynamic decision-making problems that involve hesitation and uncertainty.

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.