论文标题
通过分支机构和随机动态编程的混合体计算最佳（R，S，S）策略参数
Computing Optimal (R, s, S) Policy Parameters by a Hybrid of Branch-and-Bound and Stochastic Dynamic Programming
论文作者
论文摘要
随机库存控制中的一项知名控制策略是（R，S，S）策略，在该策略中，每当它低于重订单级别的s以下时，将库存提高到订购级别s。迄今为止，很少或没有工作专门用于开发计算方法（R，S，S）策略参数。在这项工作中，我们引入了一种混合方法，该方法利用树搜索来计算最佳的补充周期，并在给定的周期中计算（S，S）水平。多达99.8％的搜索树是通过动态编程产生的分支和界限来修剪的。一项数值研究表明，该方法可以在合理的时间内解决现实规模的实例。
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.
