报告题目：Supply Chain Visibility: Impact and Value of Real-time Resource Allocation
报 告 人：吕国栋 香港科技大学
报告时间：2023年5月24日(周三) 16:00-16:30
报告地点：中国科学院大学中关村校区教学楼S406
腾讯会议：873-9767-9281
内容摘要
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 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.
主讲人简介
Dr. Guodong Lyu is an Assistant Professor at the School of Business and Management, Hong Kong University of Science and Technology. He is broadly interested in exploring the interactions between data analytics and operations research, with applications stretching from classical operation management to smart city operations and sustainable development. His focus of methodology includes online optimization, robust optimization, empirical analysis, and machine learning. In his research, he has been collaborating with different government agencies and companies, including IMDA, P&G, Roambee, SingPost, Didi Chuxing, Yamato, and Singapore Pools, etc. His research has been published in journals including Management Science, Operations Research, and Manufacturing & Service Operations Management. He received his Ph.D. degree from NUS Business School. His Ph.D. dissertation was a finalist in INFORMS George B. Dantzig Dissertation Award Competition 2019.
