In this paper we study the performance of a two-stage approach to scheduling under uncertainty making use of sequences of groups of permutable operations. Given a sample set of uncertainty realization scenarios, the goal is to compute a sequence of groups of permutable operations representing a partial scheduling decision in the first-stage, that yields the best possible score in the second-stage, when, for a specific scenario, a full operation sequence is obtained via a secondstage decision policy. This approach is described for a single machine problem and the jobshop problem with stochastic and robust optimization, as well as several commonly studied objectives. We propose new constraint programming models as well as a genetic algorithm meta-heuristic to compute such two-stage solutions. We also investigate a warm-start scheme to work around the difficult search space of sequences of permutable operations. Experiments are carried out to characterize when this two-stage approach yields better results. We also compare the introduced methods with existing ones. Theoretical extensions of the known methods are also described and evaluated.