Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

Owing to the increasing scarcity of fossil fuels and environmental concerns, renewable energy sources have gained significant attraction. In this study, we explored multi-objective function optimization to realize a strategic and tactical biofuel supply chain considering greenhouse gas emissions and total annualized cost. The advanced version of the augmented epsilon-constraint method, denoted as AUGMECON-R, was used to trade-off among the competing objectives and obtain feasible solutions to achieve desired levels of sustainability. The TOPSIS method was used to incorporate decision-making preferences based on their similarity to ideal solutions. According to the results, a sustainable solution has a levelized cost of $6.63 per gallon of gasoline-equivalent (GGE) and emits 674.83 grams of CO2-equivalents per GGE. Sensitivity analysis was also performed to evaluate the effects of variations in cost parameters, feedstock availability, and biofuel demand. According to the sensitivity results, lignocellulosic biomass demand is the most sensitive parameter. Finally, the model is applied to a case study to demonstrate its applicability and efficiency.