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<p>Waste treatment transforms waste into valuable resources, addressing environmental challenges while supporting sustainable practices in energy recovery, resource management, and pollution control. This study addresses the selection of appropriate “Food Waste Treatment Method” (FWTM), an important component in sustainability that requires effective management. The existing models of FWTM selection have some limitations that include (i) inadequate handling of uncertainty, (ii) insufficient systemic determination of experts’ importance, and (iii) lack of customized ranking based on user preferences. To address these gaps in selecting FWTM, this study proposes an integrated and personalized “Multi-Criteria Decision-Making” (MCDM) framework. In this framework, “q-Rung Orthopair Fuzzy Set” (qROFS) has been utilized to manage data uncertainty since it presents the ratings of FWTM based on various criteria as a tuple containing a degree of preference and non-preference. To systematically determine the expert weights and criteria weights, “Linear Programming” (LP) and “LOgarithmic Percentage Change-driven Objective Weighting” (LOPCOW) have been used, respectively. A novel extension of “COmplex PRopotional ASsessment” (COPRAS), a query-based COPRAS, has been used in this framework. This extension of COPRAS offers adaptability and personalization in the selection of FWTM since it also considers the user’s preference for the kind of FWTM based on the user’s requirements and specifications. A case study is also presented, which helps understand the model’s applicability. Notably, from the selected FWTMs, the model identified anaerobic digestion as an optimal FWTM, followed by incineration and heat-moisture reaction. Sensitivity and comparative analyses are performed to understand the strengths and weaknesses of the model. Results from the sensitivity analysis of queries infer that there is a strong effect of query vector(s) on the ranking of FWTMs. As the number of queries increases, rank orders at certain positions stabilize, indicating that the demand from multiple sources converges. Further, it is noted that anaerobic digestion is highly preferred, and composting is less preferred in this study. The global contribution of this study lies in providing a dynamic, context-sensitive framework for selecting food waste treatment methods, enabling optimized decision-making tailored to diverse environmental, social, and economic scenarios.</p>

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Selection of waste treatment methods for food sources: an integrated decision model using q-rung fuzzy data, LOPCOW, and COPRAS techniques

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Sundararajan Dhruva,
  • Raghunathan Krishankumar,
  • Kattur Soundarapandian Ravichandran,
  • Arturas Kaklauskas,
  • Edmundas Kazimieras Zavadskas,
  • Pankaj Gupta

摘要

Waste treatment transforms waste into valuable resources, addressing environmental challenges while supporting sustainable practices in energy recovery, resource management, and pollution control. This study addresses the selection of appropriate “Food Waste Treatment Method” (FWTM), an important component in sustainability that requires effective management. The existing models of FWTM selection have some limitations that include (i) inadequate handling of uncertainty, (ii) insufficient systemic determination of experts’ importance, and (iii) lack of customized ranking based on user preferences. To address these gaps in selecting FWTM, this study proposes an integrated and personalized “Multi-Criteria Decision-Making” (MCDM) framework. In this framework, “q-Rung Orthopair Fuzzy Set” (qROFS) has been utilized to manage data uncertainty since it presents the ratings of FWTM based on various criteria as a tuple containing a degree of preference and non-preference. To systematically determine the expert weights and criteria weights, “Linear Programming” (LP) and “LOgarithmic Percentage Change-driven Objective Weighting” (LOPCOW) have been used, respectively. A novel extension of “COmplex PRopotional ASsessment” (COPRAS), a query-based COPRAS, has been used in this framework. This extension of COPRAS offers adaptability and personalization in the selection of FWTM since it also considers the user’s preference for the kind of FWTM based on the user’s requirements and specifications. A case study is also presented, which helps understand the model’s applicability. Notably, from the selected FWTMs, the model identified anaerobic digestion as an optimal FWTM, followed by incineration and heat-moisture reaction. Sensitivity and comparative analyses are performed to understand the strengths and weaknesses of the model. Results from the sensitivity analysis of queries infer that there is a strong effect of query vector(s) on the ranking of FWTMs. As the number of queries increases, rank orders at certain positions stabilize, indicating that the demand from multiple sources converges. Further, it is noted that anaerobic digestion is highly preferred, and composting is less preferred in this study. The global contribution of this study lies in providing a dynamic, context-sensitive framework for selecting food waste treatment methods, enabling optimized decision-making tailored to diverse environmental, social, and economic scenarios.