BiorefinerySupply Chain Network Design under CompetitiveFeedstock Markets: An Agent-Based Simulation and Optimization Approach.

Weaddress the problem of biorefinery supply chain network designunder competitive corn markets. Unlike existing methods, the purchaseprices of corn are considered to vary not only across time but alsoacross competing biorefineries in a given region for all time periodsin the design horizon. As the feedstock cost for purchasing corn isthe largest cost component for producing ethanol, it is critical toconsider the formation of corn prices in real-world markets involvingcompetition and interactions among biorefineries, among farmers, andbetween biorefineries and the food market. However, these competitivemarkets are difficult to formulate in a mathematical program. To simulatethe corn markets, an agent-based model is developed. In each market,the dynamic corn prices are determined by a double-auction processparticipatedin by biorefinery agents,farmer agents, and a food market agent. The determined corn pricesare then returned to the supply chain design problem, which is a mixed-integernonlinear program (MINLP) with black-box functions. However, sucha problem cannot be solved directly by a MINLP solver. Thus, we usea genetic algorithm to solve the optimization problem and determinethe location and capacity of each biorefinery in the network. Theproposed method is demonstrated by a case study on a corn-based biorefinerysupply chain network design in Illinois in which the optimal net presentvalue of a network of 10 biorefineries increased by 10.7% comparedto that of the initial supply chain network. [ABSTRACT FROM AUTHOR]