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<p><i>t</i>-spherical fuzzy sets (<i>t</i>-SFSs) provide a generalized framework for representing data uncertainties through membership, neutral, and non-membership degrees, effectively capturing diverse forms of vague and uncertain information in complex multi-criteria decision-making (MCDM) scenarios. The manuscript develops a hybrid approach for prioritizing criteria using prioritized operators combined with smooth approximation through Aczel–Alsina (AA) operations. New aggregation operators-<i>t</i>-spherical fuzzy AA prioritized average, <i>t</i>-spherical fuzzy AA prioritized geometric, <i>t</i>-spherical fuzzy AA prioritized weighted average, and <i>t</i>-spherical fuzzy AA prioritized weighted geometric-are introduced. Their properties, including monotonicity, idempotency, and boundary behavior, are examined. The study also addresses the limitations and biases of single-weight determination methods by integrating objective entropy weights with subjective pivot pairwise relative criteria importance assessment (PIPRECIA) weights. An integrated MCDM algorithm, developed using the proposed operators and the combined criteria weight determination model, is applied to a detailed case study on subject expert selection in medicine. The case study evaluates the algorithm’s effectiveness, explores the impact of variable parameters on decision-making, and ensures stability in ranking results. Finally, a detailed comparative analysis with existing aggregation operators is carried out to highlight the significance of the devised methodology.</p>

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Multi-criteria decision-making method based on an integrated model using T-spherical fuzzy aczel-alsina prioritized aggregation operators

  • 期刊论文

  • 发表日期：

    2025年2月28日

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  • Jawad Ali

摘要

t-spherical fuzzy sets (t-SFSs) provide a generalized framework for representing data uncertainties through membership, neutral, and non-membership degrees, effectively capturing diverse forms of vague and uncertain information in complex multi-criteria decision-making (MCDM) scenarios. The manuscript develops a hybrid approach for prioritizing criteria using prioritized operators combined with smooth approximation through Aczel–Alsina (AA) operations. New aggregation operators-t-spherical fuzzy AA prioritized average, t-spherical fuzzy AA prioritized geometric, t-spherical fuzzy AA prioritized weighted average, and t-spherical fuzzy AA prioritized weighted geometric-are introduced. Their properties, including monotonicity, idempotency, and boundary behavior, are examined. The study also addresses the limitations and biases of single-weight determination methods by integrating objective entropy weights with subjective pivot pairwise relative criteria importance assessment (PIPRECIA) weights. An integrated MCDM algorithm, developed using the proposed operators and the combined criteria weight determination model, is applied to a detailed case study on subject expert selection in medicine. The case study evaluates the algorithm’s effectiveness, explores the impact of variable parameters on decision-making, and ensures stability in ranking results. Finally, a detailed comparative analysis with existing aggregation operators is carried out to highlight the significance of the devised methodology.