Tight Bounds for a Stochastic Resource Allocation Algorithm Using Marginal Revenue

This paper contributes to solve effectively stochastic resource allocation problems known to be NP-Complete. To address this complex resource management problem, previous works on pruning the action space of real-time heuristic search is extended. The pruning is accomplished by using upper and lower bounds on the value function. This way, if an action in a state has its upper bound lower than the lower bound on the value of this state, this action may be pruned in the set of possible optimal actions for the state. This paper extends this previous work by proposing tight bounds for problems where tasks have to be accomplished using limited resources. The marginal revenue bound proposed in this paper compares favorably with another approach which proposes bounds for pruning the action space.