  A well-know 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.
