This thesis consists in revisiting traditional scheduling problematics in computational environments, and considering the adjunction of uncertainty in the models. We adopt here a wide definition of uncertainty that encompasses the intrinsic stochastic nature of some phenomena (e.g., processor failures that follow a Poissonian distribution) and the imperfection of model characteristics (e.g., inaccuracy of the costs in a model due to a bias in measurements). We also consider uncertainties that stem from indeterminations such as the user behaviors that are uncontrolled although being deterministic. Scheduling, in its general form, is the operation that assigns requests to resources in some specific way. In distributed environments, we are concerned by a workload (i.e., a set of tasks) that needs to be executed onto a computational platform (i.e., a set of processors). Therefore, our objective is to specify how tasks are mapped onto processors. Produced schedules can be evaluated through many different metrics (e.g., processing time of the workload, resource usage, etc) and finding an optimal schedule relatively to some metric constitutes a challenging issue. Probabilistic tools and multi-objectives optimization techniques are first proposed for tackling new metrics that arise from the uncertainty. In a second part, we study several uncertainty-related criteria such as the robustness (stability in presence of input variations) or the reliability (probability of success) of a schedule.