Dynamic Safety-Stocks for Asymptotic Optimality in Stochastic Networks
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This paper concerns control of stochastic networks using state-dependent safety-stocks.
These results are based on the construction of an approximate solution to the average-cost dynamic programming equations using a perturbation of the value function for an associated fluid model.
Moreover, a new technique is introduced to obtain fluid-scale asymptotic optimality for general networks modeled in discrete time. The proposed policy is myopic with respect to a perturbation of the value function for the fluid model.
Keywordsqueueing networks routing scheduling optimal control
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