Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGlS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal(-1), considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations. (C) 2017 Elsevier Ltd. All rights reserved.