报告题目：Asymptotically Optimal Distributionally Robust Solutions through Forecasting and Operations Decentralization
报告人：龙卓瑜 副教授
邀请人：胡祥培 教授
报告时间及地点：2025年7月10日（周四） 下午16:40-18：00 经济管理学院B305
报告人简介：
Daniel Zhuoyu Long is an Associate Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. Previously, he received his Bachelor's degree from Tsinghua University in 2005, Master's degree from the Chinese Academy of Sciences in 2008, and Ph.D. from the National University of Singapore Business School in 2013, joining CUHK in the same year. His research primarily focuses on distributed robust optimization theory and its applications to various operations management problems, such as logistics and supply chain management, project management, healthcare operations management, and revenue management. His work was elected as a finalist for the 2021 Best OM Paper in OR, and received the 2022 CSAMSE Best Paper Award (First Prize) and 2024 CSAMSE Best Paper Award (Second Prize). He currently serves as an Associate Editor for the MSOM Journal.
讲座简介：
Two-stage risk-averse distributionally robust optimization (DRO) problems are ubiquitous across many engineering and business applications. In these problems, decision-makers commit to capacitated first-stage decisions, anticipating that they can execute second-stage recourse decisions after observing the realization of the uncertain parameters. Despite their promising resilience, two-stage DRO problems are generally computationally intractable. To address this challenge, we propose a simple framework by decentralizing the decision-making process into two specialized teams: forecasting and operations. This decentralization aligns with prevalent organizational practices, in which the operations team uses the information communicated from the forecasting team as input to make decisions. We formalize this decentralized procedure as a bilevel problem to design a communicated distribution that can yield asymptotic optimal solutions to original two-stage risk-averse DRO problems. We identify an optimal solution that is surprisingly simple. The forecasting team only needs to communicate a two-point distribution to the operations team. Consequently, the operations team can solve a highly tractable and scalable optimization problem to identify asymptotic optimal solutions. Specifically, as the magnitude of the problem parameters (including the uncertain parameters and the first-stage capacity) increases to infinity at an appropriate rate, the cost ratio between our induced solution and the original optimal solution converges to one, indicating that our decentralized approach yields high-quality solutions. We compare our decentralized approach against the truncated linear decision rule approximation and demonstrate that our approach has broader applicability and superior computational efficiency while maintaining competitive performance. Using real-world sales data, we have demonstrated the practical effectiveness of our strategy. The finely tuned solution significantly outperforms traditional sample-average approximation methods in out-of-sample performance.
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