A comprehensive approach to optimize lignocellulosic biomass processing for cost-effective biofuel production

As the global population continues to grow exponentially, the demand for renewable energy sources is more critical than ever. Bioethanol, derived from lignocellulosic biomass, has emerged as a promising alternative to fossil fuels. This study focuses on optimizing bioethanol production using a supply chain model that incorporates corn stover, sugarcane bagasse, and miscanthus as feedstocks. To minimize overall supply chain costs, a mixed-integer linear programming (MILP) model is developed, considering key cost factors such as feedstock procurement, bioethanol production, transportation, and facility installation. Traditional optimization methods are replaced with the Social Group Optimization (SGO) algorithm to enhance computational efficiency and solution quality. The results demonstrate that SGO achieves lower total costs with faster convergence, making it an effective optimization approach. Sensitivity analysis reveals that feedstock procurement has the highest impact on total supply chain costs, while production costs show moderate sensitivity, and installation costs have also sensitive effect. These findings emphasize the importance of strategic feedstock sourcing and production planning for economically viable bioethanol supply chains. The proposed SGO-based framework offers a practical solution for industry practitioners seeking cost-effective and sustainable bioethanol production. Future research could explore uncertainty modeling, environmental impact assessments, and hybrid metaheuristic approaches to further enhance sustainable supply chain decision-making.