| Abstract: | 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. |
