LAUSR

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the $$\hbox {CO}_{2}$$CO2 emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented $$\epsilon $$ϵ-constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production.