Bioenergy sources have been introduced as a means to address challenges of conventional energy sources. The uncertainties of supply-side (upstream) externalities (e.g., collection and logistics) represent the key challenges in bioenergy supply chains and lead to reduce cross-cutting sustainability benefits. We propose a mixed biomass-based energy supply chain (consisting of mixed-mode bio-refineries and mixed-pathway transportation) and a multi-criteria decision making framework to address the upstream challenges. Our developed framework supports decisions influencing the economic and environmental dimensions of sustainability. Economic analysis employs a support vector machine technique, to predict the pattern of uncertainty parameters, and a stochastic optimization model, to incorporate uncertainties into the model. The stochastic model minimizes the total annual cost of the proposed mixed supply chain network by using a genetic algorithm. Environmental impact analysis employs life cycle assessment to evaluate the global warming potential of the cost-effective supply chain network. Our presented approach is capable of enhancing sustainability benefits of bioenergy industry infrastructure. A case study for the Pacific Northwest is used to demonstrate the application of the methodology and to verify the models. The results indicate that mixed supply chains can improve sustainability performance over traditional supply infrastructures by reducing costs (up to 24%) and environmental impacts (up to 5%).