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<p>Identifying a sustainable healthcare waste management (HWM) technique requires balancing environmental, economic, social and technical factors. Such evaluations involve uncertainty and diverse expert opinions, making the problem a multi-criteria group decision-making (MCGDM) scenario. A key limitation of existing models is the assumption that all expert judgements in diverse criteria are equally reliable. This study addresses this gap by proposing a novel picture fuzzy decision-support framework that explicitly models expert confidence levels. Two confidence-based Dombi aggregation operators are introduced to weight expert evaluations according to their confidence. This allows more credible opinions to exert a stronger influence while preserving group consensus. Unknown expert weights are determined using the rank-sum method. Unknown criteria weights are obtained by integrating “method based on the removal effects of criteria (MEREC)” and “ranking comparison (RANCOM)” using a Bayesian approach, avoiding subjective parameters and improving weight consistency. To rank alternatives, the “ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI)” method is extended to the picture fuzzy environment context. The results consistently identify “autoclaving” as the most sustainable HWM method, followed by “microwave” and “plasma pyrolysis”. The method’s stability and reliability are verified through sensitivity and comparative analyses with existing MCGDM methods and parameter variations, respectively. The findings demonstrate that the proposed method improves decision robustness, and offers a practical tool for policymakers and health administrators to improve resource allocation and sustainability in HWM systems.</p>

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A novel confidence level-based RANCOM-MEREC-RAFSI integrated picture fuzzy approach for sustainable healthcare waste management

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Mijanur Rahaman Seikh,
  • Prayosi Chatterjee

摘要

Identifying a sustainable healthcare waste management (HWM) technique requires balancing environmental, economic, social and technical factors. Such evaluations involve uncertainty and diverse expert opinions, making the problem a multi-criteria group decision-making (MCGDM) scenario. A key limitation of existing models is the assumption that all expert judgements in diverse criteria are equally reliable. This study addresses this gap by proposing a novel picture fuzzy decision-support framework that explicitly models expert confidence levels. Two confidence-based Dombi aggregation operators are introduced to weight expert evaluations according to their confidence. This allows more credible opinions to exert a stronger influence while preserving group consensus. Unknown expert weights are determined using the rank-sum method. Unknown criteria weights are obtained by integrating “method based on the removal effects of criteria (MEREC)” and “ranking comparison (RANCOM)” using a Bayesian approach, avoiding subjective parameters and improving weight consistency. To rank alternatives, the “ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI)” method is extended to the picture fuzzy environment context. The results consistently identify “autoclaving” as the most sustainable HWM method, followed by “microwave” and “plasma pyrolysis”. The method’s stability and reliability are verified through sensitivity and comparative analyses with existing MCGDM methods and parameter variations, respectively. The findings demonstrate that the proposed method improves decision robustness, and offers a practical tool for policymakers and health administrators to improve resource allocation and sustainability in HWM systems.