We address the problem of biorefinery supply chain network design under competitive corn markets. Unlike existing methods, the purchase prices of corn are considered to vary not only across time but also across competing biorefineries in a given region for all time periods in the design horizon. As the feedstock cost for purchasing corn is the largest cost component for producing ethanol, it is critical to consider the formation of corn prices in real-world markets involving competition and interactions among biorefineries, among farmers, and between biorefineries and the food market. However, these competitive markets are difficult to formulate in a mathematical program. To simulate the corn markets, an agent-based model is developed. In each market, the dynamic corn prices are determined by a double-auction process participated in by biorefinery agents, farmer agents, and a food market agent. The determined corn prices are then returned to the supply chain design problem, which is a mixed-integer nonlinear program (MINLP) with black-box functions. However, such a problem cannot be solved directly by a MINLP solver. Thus, we use a genetic algorithm to solve the optimization problem and determine the location and capacity of each biorefinery in the network. The proposed method is demonstrated by a case study on a corn-based biorefinery supply chain network design in Illinois in which the optimal net present value of a network of 10 biorefineries increased by 10.7% compared to that of the initial supply chain network.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering