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<p>The study proposes a theoretical framework for handling uncertainty, developed using continuous function intuitionistic fuzzy sets (CFIFSs). This framework incorporates novel elements, including Gaussian aggregation operators, a new score function, and a novel cross-entropy measure, designed to improve the processing of uncertainty. The Gaussian aggregation operators ensure smooth and flexible fusion of intuitionistic membership information. The proposed cross-entropy measure facilitates a more sensitive and consistent similarity assessment between CFIFSs. These components are integrated into a multi-criteria decision-making (MCDM) framework, where the Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to determine objective criterion weights, and the Multi-Attributive Border Approximation Area Comparison (MABAC) method is adapted to operate under the CFIFS structure. The proposed approach is applied to an e-waste management problem from the literature involving the selection of Industry 4.0 technologies. The findings demonstrate that the model generates stable and interpretable rankings while providing a robust and flexible decision-making framework for managing uncertainty.</p>

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Gaussian Aggregation and Cross-Entropy within Continuous Function Intuitionistic Fuzzy Sets Integrated with the CRITIC-MABAC Model for E-Waste Management

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Büşra Aydoğan,
  • Mehmet Ünver

摘要

The study proposes a theoretical framework for handling uncertainty, developed using continuous function intuitionistic fuzzy sets (CFIFSs). This framework incorporates novel elements, including Gaussian aggregation operators, a new score function, and a novel cross-entropy measure, designed to improve the processing of uncertainty. The Gaussian aggregation operators ensure smooth and flexible fusion of intuitionistic membership information. The proposed cross-entropy measure facilitates a more sensitive and consistent similarity assessment between CFIFSs. These components are integrated into a multi-criteria decision-making (MCDM) framework, where the Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to determine objective criterion weights, and the Multi-Attributive Border Approximation Area Comparison (MABAC) method is adapted to operate under the CFIFS structure. The proposed approach is applied to an e-waste management problem from the literature involving the selection of Industry 4.0 technologies. The findings demonstrate that the model generates stable and interpretable rankings while providing a robust and flexible decision-making framework for managing uncertainty.