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<p>Transforming food waste into bioenergy reduces the reliance on fossil fuels while supporting circular economy principles. The study develops a fuzzy optimization model to design a sustainable food waste-to-bioenergy supply chain under uncertainty while considering government subsidies and behavioural parameters influencing food waste return. The effectiveness of awareness and incentive mechanisms is modeled using a sigmoid function to depict the food waste return rate. The multi-objective framework integrates economic, environmental, and social objectives under the triple bottom line framework. The model is optimized using the non-dominated sorting genetic algorithm-II to generate Pareto-optimal trade-offs among competing sustainability goals. The uncertain factors, such as biogas yield, incentives perception, awareness effectiveness, and energy demands, are represented using trapezoidal fuzzy numbers. Results indicate that the optimized systems achieve a higher food waste return rate, reflecting effective behavioural participation in the collection system. Smart bioenergy production improves operational and environmental performance, although it requires approximately 9.6% higher upfront capital investment due to additional equipment and infrastructure requirements. Government subsidies also lower the total cost of the system and stabilize investment costs. Life cycle assessment results further highlight the environmental benefits of diverting food waste from landfills towards renewable bioenergy production. The proposed framework enhances the resilience and robustness of the supply chain and provides decision support to policymakers and urban planners in designing sustainable, incentive-driven bioenergy systems under uncertain conditions.</p>

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Multi-objective optimization and life cycle assessment of smart bioenergy production from food waste under behavioural uncertainty

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Arzo Rani,
  • Abhinav Goel,
  • Anand Chauhan

摘要

Transforming food waste into bioenergy reduces the reliance on fossil fuels while supporting circular economy principles. The study develops a fuzzy optimization model to design a sustainable food waste-to-bioenergy supply chain under uncertainty while considering government subsidies and behavioural parameters influencing food waste return. The effectiveness of awareness and incentive mechanisms is modeled using a sigmoid function to depict the food waste return rate. The multi-objective framework integrates economic, environmental, and social objectives under the triple bottom line framework. The model is optimized using the non-dominated sorting genetic algorithm-II to generate Pareto-optimal trade-offs among competing sustainability goals. The uncertain factors, such as biogas yield, incentives perception, awareness effectiveness, and energy demands, are represented using trapezoidal fuzzy numbers. Results indicate that the optimized systems achieve a higher food waste return rate, reflecting effective behavioural participation in the collection system. Smart bioenergy production improves operational and environmental performance, although it requires approximately 9.6% higher upfront capital investment due to additional equipment and infrastructure requirements. Government subsidies also lower the total cost of the system and stabilize investment costs. Life cycle assessment results further highlight the environmental benefits of diverting food waste from landfills towards renewable bioenergy production. The proposed framework enhances the resilience and robustness of the supply chain and provides decision support to policymakers and urban planners in designing sustainable, incentive-driven bioenergy systems under uncertain conditions.