论文标题
（R，S，S）策略参数计算的随机动态编程启发式
Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation
论文作者
论文摘要
（R，S，S）是从业者广泛使用的随机库存控制策略。在根据本政策管理的库存系统中，库存将在即时r上进行审查；如果观察到的库存位置低于重订单级别S，则下达订单。该订单的数量设置为将库存位置提高到订单到级别S。本文引入了基于新的随机动态程序（SDP）启发式程序，以计算出（R，S，S，S）策略参数，用于非常规的随机批量大小尺寸问题，并以积压的要求，固定的订单，固定订单，固定订单，固定的订单，固定订单，固定订单和固定成本和线性成本和线性成本和固定成本和固定成本，以及固定成本和固定成本，以及固定成本和固定成本。在最近的一项工作中，Visentin等。 （2021）提出了一种在这些假设下计算最佳策略参数的方法。我们的模型结合了对问题的贪婪放松与围巾（S，S）SDP的修改版本。该模型的简单实现需要进行过度的计算工作来计算参数。但是，我们可以使用K-Convexity属性和记忆技术加快计算加快计算。由此产生的算法要比最先进的算法要快得多，从而扩展了从业者的可采用性。一项广泛的计算研究将我们的方法与文献中可用的算法进行了比较。
The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order's quantity is set to raise the inventory position to the order-up-to-level S. This paper introduces a new stochastic dynamic program (SDP) based heuristic to compute the (R, s, S) policy parameters for the non-stationary stochastic lot-sizing problem with backlogging of the excessive demand, fixed order and review costs, and linear holding and penalty costs. In a recent work, Visentin et al. (2021) present an approach to compute optimal policy parameters under these assumptions. Our model combines a greedy relaxation of the problem with a modified version of Scarf's (s, S) SDP. A simple implementation of the model requires a prohibitive computational effort to compute the parameters. However, we can speed up the computations by using K-convexity property and memorisation techniques. The resulting algorithm is considerably faster than the state-of-the-art, extending its adoptability by practitioners. An extensive computational study compares our approach with the algorithms available in the literature.
