Navigating epistemic uncertainty in third-generation biodiesel supply chain management through robust optimization for economic and environmental performance

The escalating global warming crisis and the dwindling traditional energy sources have spurred an urgent need for exploring sustainable and renewable energy options. In this context, third-generation biodiesel has emerged as a promising and cleaner alternative for the transportation sector, gaining widespread attention due to its environmental and social benefits. However, ensuring the sustainable and efficient production and delivery of biodiesel to the market requires an optimal supply chain management strategy, encompassing a coordinated effort by all stakeholders involved. This paper presents a novel multi-objective decision support model for managing a multi-echelon third-generation biodiesel supply chain comprising a biomass producer, a biorefinery, and multiple retailers. The proposed model leverages a multi-objective non-linear optimization framework, incorporating economic and environmental objectives to achieve a balance between profitability and sustainability. The economic performance of the model is analyzed using an uncertain activity-based costing approach and a single-setup-multi-delivery policy, while carbon emissions are reduced at every stage of the supply chain to enhance environmental performance. To deal with the uncertainties in the decision-making parameters, a robust possibilistic programming-based solution methodology is employed, providing optimality and feasibility robustness to the obtained solutions. Furthermore, a weighted goal programming approach is adopted to achieve an optimal trade-off between the conflicting objectives of the model, and the NP-hard problem is solved using a metaheuristic approach. The practical applicability of the proposed model is demonstrated through an experimental study, and the performance of the numerical outcomes is analyzed under varying decision-maker preferences and sensitivity analysis. The experimental findings indicate that SSMD reduces the total cost of the system. The proposed approach can be used to evaluate the environmental, economic, and technological factors that can enhance supply chain management performance.