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<p>This research introduces a novel multi-criteria decision-making (MCDM) framework for addressing ambiguity and uncertainty in practical scenarios by utilizing interval-valued bipolar fuzzy (IVBF) sets in conjunction with Aczel–Alsina (AA) aggregation operators. First, new operational laws for IVBF numbers are defined, which serve as the foundation for constructing a family of AA-based aggregation operators. These operators are then examined for key properties, including idempotency, monotonicity, and boundedness, to establish their theoretical soundness. Building on this foundation, a comprehensive IVBF-AA MCDM method is proposed and applied to a real-world waste management planning problem. To assess its performance, the results are compared with those of existing approaches, demonstrating the robustness and superiority of the proposed method in handling uncertainty and complex decision environments.</p>

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Multi-criteria decision making using Aczel-Alsina operators in interval-valued bipolar fuzzy context for waste management planning

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Madiha Ghamkhar,
  • Aasma Tariq,
  • Muhammad Azeem,
  • Jawad Ali

摘要

This research introduces a novel multi-criteria decision-making (MCDM) framework for addressing ambiguity and uncertainty in practical scenarios by utilizing interval-valued bipolar fuzzy (IVBF) sets in conjunction with Aczel–Alsina (AA) aggregation operators. First, new operational laws for IVBF numbers are defined, which serve as the foundation for constructing a family of AA-based aggregation operators. These operators are then examined for key properties, including idempotency, monotonicity, and boundedness, to establish their theoretical soundness. Building on this foundation, a comprehensive IVBF-AA MCDM method is proposed and applied to a real-world waste management planning problem. To assess its performance, the results are compared with those of existing approaches, demonstrating the robustness and superiority of the proposed method in handling uncertainty and complex decision environments.