Scheduling tasks with failure probabilities to minimize expected cost

A project is made of several tasks which can be executed in parallel. Each task is associated with performance cost and failure probability. If all tasks succeed a reward is obtained. We suggest a dynamic programming algorithm to solve the problem of scheduling the tasks in order to maximize the expected discounted profit. We show that the general problem is NP-complete, but that there are cases where the dynamic programming algorithm is polynomially bounded.