Optimizing the Design and Planning of a Sugar-Bioethanol Supply Chain under Uncertain Market Conditions

In today's society, the so-called green consciousness about environmental issues has triggered an intense search for renewable energy sources. Bioenergy, obtained by transformation processes of biomass such as castor bean or sugar cane, plays a decisive role in this context, providing much of the energy used in the electricity production, heat supply, and transport sector. Sugar cane, in particular, has assured considerable economic relevance due to its multiple applications. It is commonly employed as fodder for animal feed or as raw material for producing electricity, bioethanol, sugar, molasses, and other bioproducts. Currently, the integrated management of all stages of the transformation processes of this biomass has become a major challenge due to the complex interactivity and exchanges between all the actors present in the logistics chain. In this work, a scenario-based approach built upon a mixed-integer linear programming (MILP) formulation is proposed aiming at designing and planning, under demand uncertainty, a sugar-bioethanol supply chain network whose harvesting, production, storage, and distribution activities are integrated. The model's optimization objective is to maximize the expected net present value (ENPV) when deciding on the location, size, and technologies of industrial parks and storage sites, the size of truck fleets, which markets to serve, and inventory levels, among other important issues. The adequacy and efficiency of the MILP model are illustrated through a case study based on the Brazilian sugar-bioethanol industry.