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<p>The transition to sustainable energy requires efficient and low-emission systems for biofuel production. This study presents a multi-objective optimization model for designing a lignocellulosic biofuel supply chain that minimizes both total economic cost and greenhouse gas emissions. The proposed framework incorporates multiple feedstock types–including corn stover, sugarcane bagasse, and miscanthus–and integrates decisions across procurement, preprocessing, conversion, storage, and distribution stages. The supply chain structure includes preprocessing hubs, energy labs, distribution centers, and demand zones. To handle the complexity and conflicting objectives, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to generate a diverse set of Pareto-optimal solutions. Results reveal clear trade-offs between cost and environmental performance. Decentralized facility configurations reduce emissions significantly but increase infrastructure investment, while centralized setups minimize cost at the expense of higher emissions. Sensitivity analysis on facility opening, procurement, and handling costs further highlights the impact of cost variability on supply chain behavior. Applied to a case study in the U.S. Midwest, the model shows that a decentralized configuration–activating both preprocessing hubs and energy labs–can achieve a 34.2% reduction in emissions with only a 12.6% increase in total system cost. The findings offer valuable insights for decision-makers aiming to balance economic viability with sustainability goals in biofuel logistics. The model serves as a practical tool for strategic planning under evolving market and policy environments.</p>

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Optimizing cost and environmental sustainability in lignocellulosic biofuel production using a multi objective approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Ankush Rana,
  • Amitabh Bhargava,
  • Anu Sayal

摘要

The transition to sustainable energy requires efficient and low-emission systems for biofuel production. This study presents a multi-objective optimization model for designing a lignocellulosic biofuel supply chain that minimizes both total economic cost and greenhouse gas emissions. The proposed framework incorporates multiple feedstock types–including corn stover, sugarcane bagasse, and miscanthus–and integrates decisions across procurement, preprocessing, conversion, storage, and distribution stages. The supply chain structure includes preprocessing hubs, energy labs, distribution centers, and demand zones. To handle the complexity and conflicting objectives, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to generate a diverse set of Pareto-optimal solutions. Results reveal clear trade-offs between cost and environmental performance. Decentralized facility configurations reduce emissions significantly but increase infrastructure investment, while centralized setups minimize cost at the expense of higher emissions. Sensitivity analysis on facility opening, procurement, and handling costs further highlights the impact of cost variability on supply chain behavior. Applied to a case study in the U.S. Midwest, the model shows that a decentralized configuration–activating both preprocessing hubs and energy labs–can achieve a 34.2% reduction in emissions with only a 12.6% increase in total system cost. The findings offer valuable insights for decision-makers aiming to balance economic viability with sustainability goals in biofuel logistics. The model serves as a practical tool for strategic planning under evolving market and policy environments.