Due to energy crisis and environmental concerns, alternative energy has attracted a lot of attention in both industry and academia. Biofuel is one type of renewable energy that can reduce the reliance on fossil fuel, and also help reduce environmental effect and provide social benefits. However, to deliver a competitive biofuel product requires a robust supply chain. The biofuel supply chain (BSC) consists of raw material sourcing, transporting of raw materials to pre-treatment and biorefinery sites, pre-treating the raw material, biofuel production, and transporting of the produced biofuel to the final demand zones. As uncertainties are involved throughout the supply chain, risks are introduced. We first propose a stochastic production planning model for a biofuel supply chain under demand and price uncertainties. A stochastic linear programming model is proposed and Benders decomposition (BD) with Monte Carlo simulation technique is applied to solve the proposed model. A case study compares the performance of a deterministic model and the proposed stochastic model. The results indicate that the proposed model obtain higher expected profit than the deterministic model under different uncertainty settings. Sensitivity analyses are performed to gain management insights. Secondly, a hedging strategy is proposed in a hybrid generation biofuel supply chain (HGBSC). A hedging strategy can purchase corn either through futures or spot, while the ethanol end-product sale is hedged using futures. A two-stage stochastic linear programming method with hedging strategy is proposed, and a Multi-cut Benders Decomposition Algorithm is used to solve the proposed model. Prices of feedstock and ethanol end-products are modeled as a mean reversion (MR). The results for both hedging and non-hedging are compared for profit realizations, and the hedging is better as compared to non-hedging for smaller profits. Further sensitivity analyses are conducted to provide managerial insights. Finally, sustainability concepts, which include economic, environmental, and social sustainability, are incorporated in the HGBSC. A two-stage stochastic mixed integer linear programming approach is used, and the proposed HGBSC model is solved using the Lagrangean Relaxation (LR) and Sample Average Approximation (SAA). A representative case study in North Dakota is used for this study.