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<p>Spherical fuzzy sets, a cutting-edge innovation within fuzzy logic, offer a robust approach to managing the inherent uncertainties and complexities present in decision-making scenarios. This framework enhances the expression of decision-makers hesitations by stipulating that the sum of the squares of membership, non-membership, and hesitation degrees must lie within [0, 1], while each parameter is independently confined to the [0, 1] interval. This structure allows for a more nuanced representation of uncertainty across attributes. The existing information measures for spherical fuzzy sets are giving unreasonable results in ambiguity computation, attribute weight computation, and linguistic hedges. This study introduces a novel knowledge measure for spherical fuzzy sets, leveraging all four constituent membership parameters, and demonstrates its adherence to axiomatic requirements. The proposed measure is shown to be demonstrably more practical and efficient than existing knowledge measures. Through comprehensive evaluation—including ambiguity computation, linguistic hedges, and attribute weight determination—the new measure was benchmarked against current spherical fuzzy information metrics. Results confirm its superior reliability. Additionally, we develop a spherical fuzzy extension of the Complex Proportional Assessment (COPRAS) method, integrating the proposed knowledge measure, and present a numerical case study identifying the optimal medical waste treatment procedure.</p>

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Applicability of a Spherical Fuzzy Information Measure in the Selection of the Most Suitable Medical Waste Treatment Method

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Abdul Haseeb Ganie,
  • Debashis Dutta

摘要

Spherical fuzzy sets, a cutting-edge innovation within fuzzy logic, offer a robust approach to managing the inherent uncertainties and complexities present in decision-making scenarios. This framework enhances the expression of decision-makers hesitations by stipulating that the sum of the squares of membership, non-membership, and hesitation degrees must lie within [0, 1], while each parameter is independently confined to the [0, 1] interval. This structure allows for a more nuanced representation of uncertainty across attributes. The existing information measures for spherical fuzzy sets are giving unreasonable results in ambiguity computation, attribute weight computation, and linguistic hedges. This study introduces a novel knowledge measure for spherical fuzzy sets, leveraging all four constituent membership parameters, and demonstrates its adherence to axiomatic requirements. The proposed measure is shown to be demonstrably more practical and efficient than existing knowledge measures. Through comprehensive evaluation—including ambiguity computation, linguistic hedges, and attribute weight determination—the new measure was benchmarked against current spherical fuzzy information metrics. Results confirm its superior reliability. Additionally, we develop a spherical fuzzy extension of the Complex Proportional Assessment (COPRAS) method, integrating the proposed knowledge measure, and present a numerical case study identifying the optimal medical waste treatment procedure.