TY - JOUR
T1 - Biorefinery supply chain network design under competitive feedstock markets
T2 - An agent-based simulation and optimization approach
AU - Singh, Akansha
AU - Chu, Yunfei
AU - You, Fengqi
N1 - Publisher Copyright:
© 2014 American Chemical Society.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
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U2 - 10.1021/ie5020519
DO - 10.1021/ie5020519
M3 - Article
AN - SCOPUS:84907546712
VL - 53
SP - 15111
EP - 15126
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
SN - 0888-5885
IS - 39
ER -