We consider a dynamic stochastic capacity problem in which tasks arrive according to a Poisson Process. Release times, stochastic deadlines and masses characterize tasks. A task might be scheduled within its time windows at a given time slot of bounded mass capacity. Each non-empty slot induces a deterministic setup cost. We aim to define a dynamic scheduling policy that maximizes the expected mass of scheduled tasks minus the setup costs over a long time horizon. This type of problem has many applications in planning, scheduling, and yield management. We use a rolling horizon approach: at each step, a particular case of the Multiple Knapsack with Restriction Assignment must be solved. We present and benchmark myopic heuristics, lookahead heuristics, and the exact solution provided by integer linear programming. We provide an extensive comparison in our dynamic and stochastic context. Finally, rather than searching for the most robust heuristic, we devise an algorithm selection process, based on random forests, to select the most effective heuristic for a given instance, considering its parameters. This achieves significant improvements.