Approximation algorithms for stochastic inventory control models

We consider two classical stochastic inventory control models, the periodic–review stochastic inventory control problem and the stochastic lot–sizing problem. The goal is to coordinate a sequence of orders of a single commodity, aiming to supply stochastic demands over a discrete, finite horizon with minimum expected overall ordering, holding, and backlogging costs. In this paper, we address the important problem of finding computationally efficient and provably good inventory control policies for these models in the presence of correlated, nonstationary (time–dependent), and evolving stochastic demands. This problem arises in many domains and has many practical applications in supply chain management.