A multi-objective robust optimization model to design sustainable sugarcane-to-biofuel supply network: the case of study

Abstract

Nowadays, oil price fluctuation, fossil fuel depletion, and the potential environmental impact of these energy resources are highly threatening the global economy. Developing renewable resources, hence, is quite unavoidable. Sugarcane, as a source of renewable energy, can be converted to bioethanol. Therefore, this study has proposed a mixed-integer linear programming to design an international network of the sugarcane-to-biofuel supply chain. To deal with uncertainty, the robust optimization approach is employed in order to maximize the profit earned from the bioethanol sales at the foreign/domestic markets, minimize the environmental impacts caused by these supply chain activities, and maximize this network generated employment. The multi-echelon supply chain model involves different production/storage capacities, bio-refineries technologies, and transportation modes. This supply chain configuration has specified the optimal production capacity/technology, the appropriate transportation mode in each route, and the bio-refineries’ development capacities. The biofuel export price and the domestic/foreign markets’ demands are among the SC uncertain parameters addressed through the robust possibilistic programming. Finally, the tri-objective model has been solved using an approach that considers the decision maker’s preferences; the model performance has been verified by a case study performed in Iran. To verify the robust model’s efficiency, the DLP realization model is also formulated.