Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models

In this paper, we consider two fundamental inventory models, the single–period newsvendor problem and its multiperiod extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true distributions. Under the assumption that the demand distributions are given explicitly, these models are well studied and relatively straightforward to solve. However, in most real–life scenarios, the true demand distributions are not available, or they are too complex to work with. Thus, a sampling–driven algorithmic framework is very attractive, both in practice and in theory.