Approximate solutions for a stochastic lot-sizing problem with partial customer-order information

Abstract

We address a stochastic single product manufacturing system in a make-to-stock environment with partial knowledge on future demand resulting from customers ordering in advance of their actual needs. The problem consists of determining the optimal size of a production lot to replenish inventory, so that delivery promises are met on time at the expense of minimal average costs. A Markov decision model is formulated for finding the optimal policy. However, since the optimal policy is likely to be too complex in most practical situations, we present approximate strategies for obtaining good production lot sizes. The well-known ( R, S) inventory policy is compared to two rules where production decisions take into account the available information on future customer requirements and the probabilistic characterization of orders yet to be placed. Furthermore, it is shown that the ( s, S) inventory policy is a special case of one of the rules. An extensive numerical study reveals that the newly developed strategies outperform classical inventory policies.