题　目：VC Theory for Inventory Policies

嘉　宾： Xin Linwei , Associate Professor, The University of Chicago

主持人：葛冬冬  教授 上海交通大学安泰经济与管理学院

                              院长 上海交通大学智能计算研究院

时　间：2024年6月25日（周二）14:00-15:30

地　点：上海交通大学 徐汇校区 安泰经济与管理学院A503

内容简介:

Advances in computational power and AI have increased interest in reinforcement learning approaches to inventory management. This paper provides a theoretical foundation for these approaches and investigates the benefits of restricting to policy structures that are well-established by decades of inventory theory. In particular, we prove generalization guarantees for learning several well-known classes of inventory policies, including base-stock and (s,S) policies, by leveraging the celebrated Vapnik-Chervonenkis (VC) theory. We apply the concepts of the Pseudo-dimension and Fat-shattering dimension from VC theory to determine the generalizability of inventory policies, that is, the difference between an inventory policy’s performance on training data and its expected performance on unseen data. We focus on a classical setting without contexts, but allow for an arbitrary distribution over demand sequences and do not make any assumptions such as independence over time. We corroborate our supervised learning results using numerical simulations.

Managerially, our theory and simulations translate to the following insights. First, there is a principle of “learning less is more” in inventory management: depending on the amount of data available, it may be beneficial to restrict oneself to a simpler, albeit suboptimal, class of inventory policies to minimize overfitting errors. Second, the number of parameters in a policy class may not be the correct measure of overfitting error: in fact, the class of policies defined by T time-varying base-stock levels exhibits a generalization error comparable to that of the two-parameter (s,S) policy class. Finally, our research suggests situations in which it could be beneficial to incorporate the concepts of base-stock and inventory position into black-box learning machines, instead of having these machines directly learn the order quantity actions.

演讲人简介：

Linwei Xin is an associate professor of operations management at the University of Chicago Booth School of Business. He specializes in inventory and supply chain management, where he designs cutting-edge models and algorithms that enable organizations to effectively balance supply and demand in various contexts with uncertainty.

Xin's research using asymptotic analysis to study stochastic inventory theory is renowned and has been recognized with several prestigious INFORMS paper competition awards, including First Place in the George E. Nicholson Student Paper Competition in 2015 and the Applied Probability Society Best Publication Award in 2019.Xin's recent interest focuses on AI for supply chains, driven by labor shortages, reshoring trends, global supply chain disruptions, and e-commerce growth. He leverages various tools such as neural networks, VC theory, applied probability, online optimization/learning, and random graph theory to address emerging challenges arising from AI-driven automation. His work targets problems in inventory management, robotics and automation in modern warehousing, dual-sourcing, real-time order fulfillment, omnichannel, and transportation network design. His research on implementing state-of-the-art multi-agent deep reinforcement learning techniques in Alibaba's inventory replenishment system was selected as a finalist for the INFORMS 2022 Daniel H. Wagner Prize, with over 65% algorithm-adoption rate within Alibaba's own supermarket brand Tmall Mart. His research on designing dispatching algorithms for robots in JD.com's intelligent warehouses was recognized as a finalist for the INFORMS 2021 Franz Edelman Award, with estimated annual savings in the hundreds of millions of dollars.

Xin currently serves as an associate editor for Operations Research, Management Science, Manufacturing & Service Operations Management, and Naval Research Logistics.

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