使用循环神经网络控制双源库存系统
Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
AI总结：
本文提出将库存动态与补货策略融入循环神经网络设计的NNCs，用于双源库存控制，可在普通PC上快速学得近优策略，并有效应对非平稳需求。
摘要：
库存管理中的一个关键挑战是识别能够从多个供应商处最优补货的策略。为了解决此类优化问题，库存管理者需要在给定净库存和未交付订单的情况下，决定从每个供应商处订购多少数量，从而使预期的拖欠成本、持有成本和采购成本之和最小化。60多年来，库存管理问题得到了广泛研究，然而即便是基本的双源采购问题——其中来自昂贵供应商的订单比来自常规供应商的订单到得更快——其一般形式仍然难以处理。此外，人们日益需要开发主动的、可扩展的优化算法，以便能够及时调整其建议以适应动态需求变化。在本研究中，我们从基于神经网络的优化视角来处理双源采购问题，并将库存动态及其补货（即控制）策略的信息纳入循环神经网络的设计中。我们表明，所提出的神经网络控制器（NNCs）能够在普通个人计算机上用几分钟CPU时间学习常用实例的近优策略。为了证明NNCs的多功能性，我们还表明它们能够控制具有经验性、非平稳需求分布的库存动态，而这类分布很难用其他最先进方法有效处理。我们的工作表明，对于具有非平稳需求的复杂库存管理问题，可以通过在优化过程中直接考虑库存动态的深度神经网络优化方法获得高质量解决方案。因此，我们的研究为高效管理复杂的高维库存动态开辟了新途径。
英文摘要：
A key challenge in inventory management is to identify policies that optimally replenish inventory from multiple suppliers. To solve such optimization problems, inventory managers need to decide what quantities to order from each supplier, given the net inventory and outstanding orders, so that the expected backlogging, holding, and sourcing costs are jointly minimized. Inventory management problems have been studied extensively for over 60 years, and yet even basic dual-sourcing problems, in which orders from an expensive supplier arrive faster than orders from a regular supplier, remain intractable in their general form. In addition, there is an emerging need to develop proactive, scalable optimization algorithms that can adjust their recommendations to dynamic demand shifts in a timely fashion. In this work, we approach dual sourcing from a neural network--based optimization lens and incorporate information on inventory dynamics and its replenishment (i.e., control) policies into the design of recurrent neural networks. We show that the proposed neural network controllers (NNCs) are able to learn near-optimal policies of commonly used instances within a few minutes of CPU time on a regular personal computer. To demonstrate the versatility of NNCs, we also show that they can control inventory dynamics with empirical, non-stationary demand distributions that are challenging to tackle effectively using alternative, state-of-the-art approaches. Our work shows that high-quality solutions of complex inventory management problems with non-stationary demand can be obtained with deep neural-network optimization approaches that directly account for inventory dynamics in their optimization process. As such, our research opens up new ways of efficiently managing complex, high-dimensional inventory dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
