Biofuel supply chain network design in competitive feedstock markets: An interactive possibilistic programming-based intelligent agent system

Investing in renewable energy has been a vital aspect of different governments’ attempts to handle unwanted negative social and environmental impacts of fossil-based energy production. Biomass energy production is considered one of the proper renewable alternatives to replace fossil fuels. Therefore, the optimal design of biofuel energy production networks is an essential part of strategic decision-making to enhance social, environmental, and economic aspects of attempts to produce clean energy. This paper investigates various aspects impacting biorefinery supply chain network design. This includes the possible disruptions in feedstock production as well as, market competition and interactions that can affect feedstock prices. As a result, a mathematical model is developed that minimizes the costs of network design under uncertainty in feedstock demands. The model computes the required demand of feedstock as well as other key parameters that are important in this agent-based simulation process. This is done by developing an agent-based learning approach based on the Roth-Erev method, which considers agents involved in feedstock pricing and flows between the main elements of its supply chain. Then, the final price of feedstock and the tradeoffs among the agents are applied as inputs in the mathematical model to relocate each facility in its supply chain. The data is computed and updated in the mathematical model by using the agent-based approach until the locations of the facilities in two consecutive runs stay intact. The applicability and validity of the model are investigated through a case study presentation. Moreover, the existing literature is utilized to validate each part of the proposed approach.