Decision-making problems involving multiple conflicting criteria and hesitant evaluations require weighting mechanisms that are both theoretically consistent and practically reliable. In spherical fuzzy group decision-making environments, expert importance is often assumed or externally assigned, while objective and subjective criterion weights are not systematically integrated. To address this limitation, this study proposes a new spherical fuzzy entropy measure to objectively derive expert weights directly from the decision matrix. In addition, objective entropy-based criterion weights and SWARA-based subjective assessments are coherently combined through a multiplicative aggregation structure within a TOPSIS framework. The proposed model is applied to a medical waste treatment technology selection problem. Comparative analyses and sensitivity experiments demonstrate stable rankings under different entropy formulations and aggregation strategies, while the proposed entropy exhibits stronger discriminatory capacity. These findings indicate that the framework provides a consistent and informative weighting structure for group decision-making under uncertainty.
Citation: Ebru Aydoğdu. A Spherical fuzzy entropy–driven TOPSIS framework with hybrid weighting for group decision-making[J]. AIMS Mathematics, 2026, 11(3): 7015-7046. doi: 10.3934/math.2026288
Decision-making problems involving multiple conflicting criteria and hesitant evaluations require weighting mechanisms that are both theoretically consistent and practically reliable. In spherical fuzzy group decision-making environments, expert importance is often assumed or externally assigned, while objective and subjective criterion weights are not systematically integrated. To address this limitation, this study proposes a new spherical fuzzy entropy measure to objectively derive expert weights directly from the decision matrix. In addition, objective entropy-based criterion weights and SWARA-based subjective assessments are coherently combined through a multiplicative aggregation structure within a TOPSIS framework. The proposed model is applied to a medical waste treatment technology selection problem. Comparative analyses and sensitivity experiments demonstrate stable rankings under different entropy formulations and aggregation strategies, while the proposed entropy exhibits stronger discriminatory capacity. These findings indicate that the framework provides a consistent and informative weighting structure for group decision-making under uncertainty.
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Ebru Aydoğdu. A Spherical fuzzy entropy–driven TOPSIS framework with hybrid weighting for group decision-making[J]. AIMS Mathematics, 2026, 11(3): 7015-7046. doi: 10.3934/math.2026288
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