Computing optimal (R, s, S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming

A well-known control policy in stochastic inventory control is the (R, s, S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls below reorder-level s. To date, little or no work has been devoted to developing approaches for computing (R, s, S) policy parameters. In this work, we introduce a hybrid approach that exploits tree search to compute optimal replenishment cycles, and stochastic dynamic programming to compute (s, S) levels for a given cycle. Up to 99.8% of the search tree is pruned by a branch-and-bound technique with bounds generated by dynamic programming. A numerical study shows that the method can solve instances of realistic size in a reasonable time. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

The study introduces a hybrid approach combining tree search and stochastic dynamic programming to compute (R, s, S) policy parameters. By pruning up to 99.8% of the search tree using a branch-and-bound technique with bounds generated by dynamic programming, the method can solve instances of realistic size in a reasonable time.