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Inventory control with unknown demand distribution is considered, withemphasis placed on the case involving discrete nonperishable items. We focus onan adaptive policy which in every period uses, as much as possible, the optimalnewsvendor ordering quantity for the empirical distribution learned up to thatperiod. The policy is assessed using the regret criterion, which measures theprice paid for ambiguity on demand distribution over $T$ periods. When thereare guarantees on the latter's separation from the critical newsvendorparameter $\beta=b/(h+b)$, a constant upper bound on regret can be found.Without any prior information on the demand distribution, we show that theregret does not grow faster than the rate $T^{1/2+\epsilon}$ for any$\epsilon>0$. In view of a known lower bound, this is almost the best one couldhope for. Simulation studies involving this along with other policies are alsoconducted.

Inventory Control Involving Unknown Demand of Discrete Nonperishable Items - Analysis of a Newsvendor-based Policy