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<p>Effective medical waste management is critical during global health crises like COVID-19, where sustainability and operational efficiency must coexist. This study proposes a hybrid decision-making framework integrating fuzzy Logarithm Methodology of Additive Weights (LMAW), fuzzy TOPSIS, and clustering analysis to evaluate third-party logistics (3PL) providers for medical waste disposal. Grounded in the triple-bottom-line (TBL) paradigm, the model systematically prioritizes ten providers in Saudi Arabia across economic (cost efficiency), environmental (emission reduction, recycling), and social (safety, compliance) criteria, using expert judgments and fuzzy logic to address decision-making uncertainty. Clustering categorizes providers into strategic groups based on performance scores, pricing, and recycling potential, enabling tailored management strategies. A case study in Saudi Arabia’s Aseer healthcare facilities validates the framework, identifying two distinct clusters: high-performance strategic partners and cost-efficient recyclers. As the first application of a fuzzy clustering hybrid method in medical waste logistics, this study offers a scalable tool for balancing sustainability and cost-effectiveness in healthcare systems globally.</p>

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Optimized Selection of Third-Party Logistics Providers for Medical Waste Disposal: A Fuzzy-Logic and Clustering Framework

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Sourour Aouadni,
  • Laila Messaoudi,
  • Jalel Euchi

摘要

Effective medical waste management is critical during global health crises like COVID-19, where sustainability and operational efficiency must coexist. This study proposes a hybrid decision-making framework integrating fuzzy Logarithm Methodology of Additive Weights (LMAW), fuzzy TOPSIS, and clustering analysis to evaluate third-party logistics (3PL) providers for medical waste disposal. Grounded in the triple-bottom-line (TBL) paradigm, the model systematically prioritizes ten providers in Saudi Arabia across economic (cost efficiency), environmental (emission reduction, recycling), and social (safety, compliance) criteria, using expert judgments and fuzzy logic to address decision-making uncertainty. Clustering categorizes providers into strategic groups based on performance scores, pricing, and recycling potential, enabling tailored management strategies. A case study in Saudi Arabia’s Aseer healthcare facilities validates the framework, identifying two distinct clusters: high-performance strategic partners and cost-efficient recyclers. As the first application of a fuzzy clustering hybrid method in medical waste logistics, this study offers a scalable tool for balancing sustainability and cost-effectiveness in healthcare systems globally.