Adaptive Greedy Rules for Dynamic and Stochastic Resource Capacity Allocation Problems

In this paper we briefly present a novel dynamic and stochastic model of resource allocation that generalizes a variety of problems addressed in the literature and we outline a unified methodology for designing adaptive greedy rules. Such rules are important in practice, since they may provide an easy-to-interpret and easy-to-implement solution to problems that are intractable for optimal solution due to the curse of dimensionality, or they embody an elegant optimal solution in some problems with simpler structure. We bridge the methodological gap between static/deterministic optimization and dynamic/stochastic optimization by stressing the connection between the classic knapsack problem and a group of related problems in management and stochastic scheduling unified by our model.