A Network Model to Optimize Upstream and Midstream Biomass-to-Bioenergy Supply Chain Costs

Growing awareness and concern within society over the use of and reliance on fossil fuels has stimulated research efforts in identifying, developing, and selecting alternative energy sources and energy technologies. Bioenergy represents a promising replacement for conventional energy, due to reduced environmental impacts and broad applicability. Sustainable energy challenges, however, require innovative manufacturing technologies and practices to mitigate energy and material consumption. This research aims to facilitate sustainable production of bioenergy from forest biomass and to promote deployment of novel processing equipment (mobile bio-refinery units). The study integrates knowledge from the renewable energy production and supply chain management disciplines to evaluate economic targets of bioenergy production with use of qualitative and quantitative techniques. The decision support system method employs two phases: (1) classification of potential biomass harvesting sites via decision tree analysis and (2) optimization of the supply network through a mixed integer linear programming model that minimizes the costs of upstream and midstream supply chain segments. While mobile units are shown to reduce biomass-to-bioenergy supply chain costs, production and deployment of the units is limited due to undeveloped bioenergy supply chains and quality uncertainty. It is reiterated that future research must address process-related and systemic issues in pursuit of sustainable energy technology development.