Evolutionary Optimization of Bioenergy Supply Chain Cost with Uncertain Forest Biomass Quality and Availability
Over the past few decades, bioenergy sources (e.g., bio-oil, biochar, and biofuels) have been introduced as a means to address environmental, energy security, and human health challenges attendant with fossil-based energy. Government and societal interest in bioenergy has put additional scrutiny on feedstock supply and logistics, systems analysis and integration, and cross-cutting sustainability. One of the key challenges in the biomass-to-bioenergy supply chain (B2BSC) is the uncertainties of externalities (e.g., supply, transportation, logistics, production, demand, and price) that can inhibit environmental performance and reduce competitiveness and robustness. The study presented herein aims to develop a multi-criteria decision making method capable of helping investigators to fill key gaps in B2BSC decision support systems. The methodology employs two quantitative methods: 1) the support vector machine method is used to predict the pattern of uncertainty parameters, and 2) a stochastic programming method is used to assess the role of a transportable bio-refinery and the impact of real-world uncertainties in B2BSCs. The developed stochastic optimization model considers upstream and midstream costs (i.e., fixed, variable, and labor costs) of a B2BSC and includes two stochastic constraints (i.e., quality and availability) to incorporate uncertainties into the model. The model can minimize the total cost of a B2BSC network by using a genetic algorithm approach. The results for a case from Oregon, USA indicate that incorporating quality and availability uncertainty in the model can aid in harvesting site selection to use high quality biomass and reduce collection cost.