Optimization Models for Biorefinery Supply Chain Network Design under Uncertainty

Biofuels are attracting increasing attention worldwide due to its environmental and economic benefits. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. The Renewable Fuel Standard (RFS) sets policies and mandates to support the production and consumption of biofuels. However, the uncertainty associated with these policies and regulations of biofuel production and consumption have significant impacts on the biofuel supply chain network. The goal of this research is first to determine the optimal design of supply chain for biofuel refineries in order to maximize the annual profit considering uncertainties in fuel market price, feedstock yield and logistic costs. In order to deal with the stochastic nature of the parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. This study also aims to investigate the impacts of the governmental policies and mandates on the total profit in the biofuel supply chain design problem. To achieve this goal, the two-stage stochastic programming models are developed in which conditional value at risk is considered as a risk measure to control the shortage of mandate. We apply these models for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models, and assess the results