Abstract In this paper, a bi-criterion, multi-period, stochastic mixed-integer linear programming model is proposed to address the optimal design and planning of hydrocarbon biorefinery supply chains under supply and demand uncertainties. The model accounts for diverse conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk which is measured by conditional value-at-risk and downside risk. The model determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multi-cut L-shaped decomposition approach is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through two case studies of hydrocarbon biorefinery supply chain for the State of Illinois.