A Bayesian learning procedure for the.(s, Q) inventory policy

We present an asymptotically optimal Bayesian learning procedure for the (s, Q) inventory policy, for the case when the probability distribution of lead time demand is unknown. This distribution is not required to be a member of a certain family, and the maximal lead time demand is also allowed to be unknown. The algorithm developed for this purpose Is an extension of a standard iterative procedure, which in its original form ‐in spite of claims to the contrary‐might produce solution values that are arbitrarily far away from the optimal one.