Treffer: Computing optimal ([formula omitted]) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming.

• We consider the inventory control problem under stochastic non-stationary demand. • We introduce a new algorithm to compute optimal (R, s, S) policy parameters. • The algorithm is a hybridisation of branch-and-bound and dynamic programming. • The computational results prove the performance of our method. 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. [ABSTRACT FROM AUTHOR]

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