We present a simulation framework combining robust planning with adaptive reoptimization strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional phantom subjected to systematic and random uncertainties. In the adaptive strategy proposed, the measured uncertainty scenarios and their assigned probabilities are updated to guide the robust reoptimization. In case of unpredictably large uncertainties, robust adaptive strategies manage to adjust to the unknown probability distribution. We also discuss optimal control strategies applicable in this framework.