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<p>This paper presents a novel dynamic sustainable production–inventory model specifically developed for the bagasse-based biodegradable tableware industry. The study integrates three sustainability levers within a unified optimization framework: preservation investment to control deterioration, green investment to reduce carbon-related costs, and residue-utilisation incentives to valorize sugarcane by-products. Inventory dynamics are expressed through coupled differential equations over production and consumption phases under price-dependent demand. A nonlinear profit function is optimized using a Genetic Algorithm to evaluate three scenarios: (i) preservation only, (ii) preservation with green investment, and (iii) full integration with bagasse allocation. The model achieves a 20.46% cumulative profit improvement over the base case, comprising 11.00% gain from green investment and an additional 8.53% from residue incentives. Further, deterioration is reduced by 98.15% and emission costs by 54.7%, demonstrating both operational and environmental efficiency. This is the first framework to jointly optimize economic, environmental, and circular-use dimensions for bagasse-based manufacturing, establishing a scalable foundation for policy-driven, low-carbon, and waste-valorizing agri-industrial systems.</p>

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Optimizing Sustainable Inventory Decisions for Bagasse-Based Tableware: A Multi-Case Model with Preservation Investment, Green Incentives, and Residue Utilization

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Prabal Das,
  • Nabendu Sen,
  • Amit Kumar Das

摘要

This paper presents a novel dynamic sustainable production–inventory model specifically developed for the bagasse-based biodegradable tableware industry. The study integrates three sustainability levers within a unified optimization framework: preservation investment to control deterioration, green investment to reduce carbon-related costs, and residue-utilisation incentives to valorize sugarcane by-products. Inventory dynamics are expressed through coupled differential equations over production and consumption phases under price-dependent demand. A nonlinear profit function is optimized using a Genetic Algorithm to evaluate three scenarios: (i) preservation only, (ii) preservation with green investment, and (iii) full integration with bagasse allocation. The model achieves a 20.46% cumulative profit improvement over the base case, comprising 11.00% gain from green investment and an additional 8.53% from residue incentives. Further, deterioration is reduced by 98.15% and emission costs by 54.7%, demonstrating both operational and environmental efficiency. This is the first framework to jointly optimize economic, environmental, and circular-use dimensions for bagasse-based manufacturing, establishing a scalable foundation for policy-driven, low-carbon, and waste-valorizing agri-industrial systems.