论文标题
学习大型供应链网络的一般库存管理政策
Learning General Inventory Management Policy for Large Supply Chain Network
论文作者
论文摘要
仓库中的库存管理直接影响制造商的利润。特别是，大型制造商生产的各种产品都由大量的零售商处理。在这种情况下，经典库存管理算法的计算复杂性非常大。近年来，基于学习的方法已经在解决此类问题方面流行。但是，先前的研究尚未是管理系统数量和零售商数量较大的系统。这项研究提出了一种基于增强学习的仓库库存管理算法，该算法可用于产品和零售商数量大的供应链系统。为了解决处理大型系统的计算问题，我们提供了在训练阶段对系统进行近似模拟的手段。我们对真实数据和人工数据的实验表明，使用近似模拟的算法可以成功处理大型供应链网络。
Inventory management in warehouses directly affects profits made by manufacturers. Particularly, large manufacturers produce a very large variety of products that are handled by a significantly large number of retailers. In such a case, the computational complexity of classical inventory management algorithms is inordinately large. In recent years, learning-based approaches have become popular for addressing such problems. However, previous studies have not been managed systems where both the number of products and retailers are large. This study proposes a reinforcement learning-based warehouse inventory management algorithm that can be used for supply chain systems where both the number of products and retailers are large. To solve the computational problem of handling large systems, we provide a means of approximate simulation of the system in the training phase. Our experiments on both real and artificial data demonstrate that our algorithm with approximated simulation can successfully handle large supply chain networks.
