A sustainable robust optimization model to design a sugarcane-based bioenergy supply network: A case study

Abstract

Reasons such as volatile fossil fuel prices, environmental limitations, and swift economic growth have increased the demand for clean renewable energy. Bioenergy has been received much attention from researchers due to its various benefits compared to other renewable energy alternatives. In this paper, a three-stage approach is developed to design a sustainable sugarcane-based bioenergy supply chain network under an uncertain environment. The fuzzy data envelopment analysis method is employed in the first stage to determine the appropriate regions for opening the sugarcane fields according to climatic, ecological, and social criteria. The nominated regions are then integrated into a mathematical optimization model as candidate locations for sugarcane fields to configure the proposed supply chain design. This stage helps reduce the computational complexity and provides a reasonable solution by excluding the inappropriate regions. In the second stage, a robust mixed-integer linear programming model (MILP) is formulated to optimize a set of strategic and tactical decision variables. The enviromental impacts from CO2 equivalent emissions (CO2-eq emissions), water, and energy consumption in all echelons of the supply chain are incorporated in the developed model. This model is capable of providing a steady design against different expected scenarios. In the last stage, an experimental analysis is conducted, to examine the performance of the developed model in comparison with an expected scenario model. A real case study in Iraq is applied to check the applicability of the developed model. The results demonstrated the superiority of the proposed robust model over the expected scenario model in terms of the mean and standard deviation of their objective functions by 18% and 51% respectively.