用深度强化学习解决供应链多源库存管理中的供应与产能风险问题。
Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
- 通过预训练的深度学习模块模拟和组合随机过程，拓展解空间。
- 在真实大规模数据集上显著提升多期多源库存管理的优化效果。
- 适合研究供应链优化与强化学习交叉应用的学者和工程师。
本文研究如何高效利用强化学习（RL）求解大规模随机优化问题，方法核心是借助干预模型更好地探索解空间。通过使用预训练的深度学习（DL）模型模拟和组合随机过程，我们将其应用于供应链优化中的一个挑战性实际问题：多源、多周期库存管理。具体而言，采用深度强化学习模型，在多种假设下学习并预测随机供应链过程。此外，引入一种约束协调机制，用于在库存网络的交叉约束条件下预测双重成本。研究表明，与直接将复杂物理约束嵌入强化学习优化问题并整体求解不同，我们的方法将供应链过程分解为可扩展、可组合的深度学习模块，从而在大型真实数据集上实现性能提升。同时，文章也指出了未来研究的开放问题，以进一步探索此类模型的有效性。
原文摘要 · Abstract (English)
In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of the proposed methodology is to better explore the solution space by simulating and composing the stochastic processes using pre-trained deep learning (DL) models. We demonstrate our approach on a challenging real-world application, the multi-sourcing multi-period inventory management problem in supply chain optimization. In particular, we employ deep RL models for learning and forecasting the stochastic supply chain processes under a range of assumptions. Moreover, we also introduce a constraint coordination mechanism, designed to forecast dual costs given the cross-products constraints in the inventory network. We highlight that instead of directly modeling the complex physical constraints into the RL optimization problem and solving the stochastic problem as a whole, our approach breaks down those supply chain processes into scalable and composable DL modules, leading to improved performance on large real-world datasets. We also outline open problems for future research to further investigate the efficacy of such models.
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