Stochastic chance-constrained multi-product multi-period optimization of sustainable biofuel supply chain: Application in a resource-scarce region

As global energy systems shift towards sustainability, optimizing biofuel supply chains in resource-scarce regions is increasingly critical. In arid areas, leveraging resilient crops and efficient logistics is essential to meet environmental and energy goals. This study addresses the critical challenge of optimizing a sustainable biofuel supply chain in one such resource-scarce area, Kerman province, Iran, focusing on the production, processing, and distribution of biofuels derived from Jatropha and Sweet Sorghum crops well-adapted to the region's arid climate. Given the complexity of tradeoffs between the economic and environmental aspects in such systems, a multi-product optimization model is developed with dual objectives to minimize both the total supply chain costs and the greenhouse gas emissions while encompassing various stages from cultivation and extraction to refining, blending, and distribution. To address uncertainties in crop yields and demand, the model incorporates stochastic chance-constrained programming. Additionally, the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method is applied to evaluate facility locations based on technical, environmental, and socio-economic factors. Our findings reveal that the import costs dominate the supply chain expenses, while site selections like Sirjan and Rafsanjan provide strategic advantages due to their favorable infrastructure and labor availability. Sensitivity analyses highlight the significant influence of crop yield, transportation costs, and demand fluctuations on both cost and emissions, underscoring the importance of resilient planning and scalable infrastructure. In comparison with baseline scenarios, the optimized model achieves a 12% reduction in total costs and a 15% decrease in transportation-related emissions.