Strategic planning and optimal tactical management of regional bioethanol supply-chain system with high spatial resolution and uncertainties

Although bioethanol is viewed as a promising alternative to traditional fossil fuels, its commercialization is hindered by high production costs and uncertain policies. This study proposes a novel integrated framework to support decision making in the bioethanol supply-chain system under complex uncertainties. By considering various geographical, economic, and environmental factors, potentially suitable sites are identified using a geographic information systems-based (GIS-based) multi-criteria decision-making approach. Then, a two-stage stochastic, fuzzy, chance-constrained programming model is developed to determine investment decisions (i.e. site and size of biorefineries) and operational strategies (i.e. production schedule, biomass supply network, etc.) in the face of uncertainties. The proposed research framework is tailored for a case study in Henan Province, China, which suggests the installation of six biorefineries with a total capacity of 679.11 million liters to reduce local carbon emissions by 1.54 million tons. Decision makers with different risk preferences can use this method to find optimal tradeoff strategies between system cost and risk under uncertainties. Sensitivity analysis also reveals that increasing the proportion of bioethanol blending targets is the key exogenous variable to promote the use of local biomass resources.