Dynamic-programming approximations for stochastic time-staged integer multicommodity-flow problems

In this paper, we consider a stochastic and time-dependent version of the min-cost integer multicommodity-flow problem that arises in the dynamic resource allocation context. In this problem class, tasks arriving over time have to be covered by a set of indivisible and reusable resources of different types. The assignment of a resource to a task removes the task from the system, modifies the resource, and generates a profit. When serving a task, resources of different types can serve as substitutes of each other, possibly yielding different revenues. We propose an iterative, adaptive dynamic-programming-based methodology that makes use of linear or nonlinear approximations of the value function. Our numerical work shows that the proposed method provides high-quality solutions and is computationally attractive for large problems.