Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks

Abstract

In this work, we propose a mixed-integer linear programming model to address the design and planning of a multi-feedstock lignocellulosic bioethanol supply chain network. In order to provide resiliency against existing epistemic uncertainties and disruption risks in the supply chain, a hybrid robust stochastic-possibilistic programming approach is employed. The proposed model minimizes total expected cost of the supply chain over non-disruption and disruption scenarios while limiting greenhouse gas emissions for sustainability considerations. The model determines the optimal supply chain strategic and tactical decision variables such as location, capacity and technology of biorefineries, transportation modes, shipments, inventory levels and production and import amounts. The performance of the model is evaluated through a real case study developed in Iran. Comparing the proposed resilient model with its non-resilient counterpart reveals that the proposed model is superior both in cost and GHG emissions reductions.