Scheduling projects with stochastic activity duration to maximize expected net present value

Although uncertainty is rife in many project management contexts, little is known about adaptively optimizing project schedules. We formulate the problem of adaptively optimizing the expected present value of a project's cash flow, and we show that it is practical to perform the optimization. The formulation includes randomness in activity durations, costs, and revenues, so the optimization leads to a recursion with a large state space even if the durations are exponentially distributed. We present an algorithm that partially exercises the "curse of dimensionality" as computational results demonstrate. Most of the paper is restricted to exponentially distributed task durations, but we sketch the adaptation of the algorithm to approximate any probability distribution of task duration.