Rossi, RobertoTarim, S. ArmaganHnich, BrahimPrestwich, Steven2023-06-162023-06-1620110925-52731873-7579https://doi.org/10.1016/j.ijpe.2010.04.017https://hdl.handle.net/20.500.14365/124615th International Symposium on Inventories -- AUG, 2008 -- Budapest, HUNGARYIn this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work achieves a significant computational efficiency and it can solve any relevant size instance in trivial time. Our method exploits the well known concept of state space relaxation. A filtering procedure and an augmenting procedure for the state space graph are proposed. Starting from a relaxed state space graph our method tries to remove provably suboptimal arcs and states (filtering) and then it tries to efficiently build up (augmenting) a reduced state space graph representing the original problem. Our experimental results show that the filtering procedure and the augmenting procedure often generate a small filtered state space graph, which can be easily processed using dynamic programming in order to produce a solution for the original problem. (C) 2010 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/openAccessInventory controlNon-stationary stochastic demandReplenishment cycle policyDynamic programmingState space relaxationState space filteringState space augmentationLot-Sizing ProblemConstraintStrategiesConference Object10.1016/j.ijpe.2010.04.0172-s2.0-79958191845