A new optimization framework is applied to management of radioactive wastes stored in belowground tanks at the US Government's Hanford, WA, nuclear fuels facility. Current remediation plans call for vitrification of the tank contents. Blending of the wastes prior to glass formation reduces the amount of material required for processing, therefore decreasing disposal costs. Uncertainty in the tank contents, the error inherent in the glass property models governing vitrification, and computational difficulties, however, render determination of an optimal tankblend assignment a challenge to existing optimization techniques. Previous studies have focused exclusively on minimization of processing and disposal costs, ignoring such management-related dimensions as the value of reducing select sources of uncertainty. Moreover, the stochastic framework employed by these studies could not guarantee that the glass property requirements were met on more than a probabilistic basis. This paper presents a more flexible, efficient, and robust optimization framework that facilitates analysis of the trade-off in reducing select sources of uncertainty. Specifically, the prediction error of the glass property models is found to be more significant than variation in tank component mass fraction estimates, and constraint violations are traced to the need to meet a limited set of glass property characteristics.