2026年第5期(总第1149期)
演讲主题：Supply Chain Visibility: Impact and Value of Real-time Resource Allocation
主讲人：吕国栋 香港科技大学商学院助理教授
主持人：马远征 供应链管理与系统工程系讲师
活动时间：2026年3月10日（周二）10:30-12:00
活动地址: 管院大楼119教室
主讲人简介：
Dr. Guodong Lyu is an Assistant Professor at HKUST Business School, The Hong Kong University of Science and Technology. He was named Star Faculty at HKUST (2024) and received the HKUST Faculty Recognition Award (2025). His research focuses on data-driven decision-making with applications in supply chains, urban transportation, logistics, and public-sector operations. Methodologically, he works on online optimization, distributionally robust optimization, and machine learning. His research work has been published in journals including Management Science, Operations Research, and Manufacturing & Service Operations Management. His research achievements have been recognized through paper awards such as Finalist in the 2019 INFORMS George B. Dantzig Dissertation Award Competition, 2024 Outstanding Paper Award from the Urban SIG of the INFORMS TSL Society, and Finalist in the 2024 Best Student Paper Competition from the College of SCM of the POMS.
活动简介：
In recent years, we have seen a surge of interest in supply chain visibility. Under this paradigm, decision-makers are able to trace the real-time data (e.g., stock level, resource allocation flow) along the entire supply chain so that they can identify the decision-making bottlenecks and take actions more efficiently. Motivated by the Gaze Heuristic, we propose a target-based online planning framework to deal with real-time resource allocation problems in both stationary and nonstationary environments. Leveraging on the Blackwell's Approachability Theorem and Online Convex Optimization tools, we characterize the near-optimal performance guarantee of our online solution in comparison with the offline optimal solution, and explore the properties of different allocation policies. We use synthetic and real-world data from various industries, from supply chain planning in manufacturing, to resource deployment in ride-sharing markets, to examine the impact and value of these real-time solutions in practice.
