报告题目：Data-Driven Robust Inventory Management with Time-Series Demand
报告人：陈植 副教授
邀请人：程春 教授
报告时间及地点：2026年3月24日 经济管理学院B205 10:00-12:00
报告人简况：
Zhi Chen is an Associate Professor in the CUHK Business School, the Chinese University of Hong Kong. His research interests include (1) designing optimization models and solving algorithms for decision-making problems in uncertain environments, targeting different levels of data availability, and applying them to practical problems in fields such as business, economics, finance, and operations; (2) exploring competition and cooperation methods in common activities such as resource allocation and risk management. His research results were published in Management Science, Operations Research, Production and Operations Management, INFORMS Journal on Computing Waiting for flagship journals. Multiple projects have received support from the National Natural Science Foundation of China and the Hong Kong Research Grants Council.
报告内容摘要：
We study the multi-period stochastic inventory management problem with time-series demand in a data-driven setting. When historical data is limited, the estimate-then-optimize method often suffers from overfitting and poor out-of-sample performance. To address this, we propose a data-driven robust optimization approach that constructs a Wasserstein ambiguity set capturing demand correlation and uncertainty across the entire planning horizon. We identify that this approach enables a recursive solution via robust dynamic programming, and we show that a state-dependent base-stock policy is robustly optimal. Statistically, we derive finite-sample performance guarantees for the data-driven robust policy relative to the full-information optimal policy, extending existing results by explicitly accounting for demand correlation and distributional uncertainty. Numerical experiments demonstrate the superior out-of-sample performance of our data-driven robust policy, particularly with limited data, and underscore the importance of modelling general time-series demand.
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