性爱影片

（单位）

林少冲（香港大学）

主持人

（单位）

何勇

讲座时间

2025年9月17日上午9:30

讲座地点

性爱影片楼B201

性爱影片 简介

Prof. Shaochong Lin is currentlyan Assistant Professor at the Department of Data and Systems Engineering, the University of Hong Kong. He received his PhD degree in Department of Decision Analytics and Operations from the College of Business, City University of Hong Kong. His current research involves data-driven Operations Management, synergies between AI and Operations Research, and their applications in real-world problems in Smart Supply Chains, Smart Transportation, Smart Retailing, and Smart Scheduling. He is proactive in cooperating with real-world companies, such as JD.com and AEON, and solving research problems motivated by practice. He has 15 publications, including practical research in leading journals, such as Production and Operations Management, IEEE Transactions on Automation Science and Engineering, Scientific Reports, Health Care Management Science, Omega, Transportation Research Part E, International Journal of Medical Informatics, and Computers & Industrial Engineering. He has obtained various best-paper awards and data analytics competition awards. He is an ad-hoc reviewer for journals such as Management Science and Operations Research. He is serving as a program committee member for the 27th AAAI Conference on Artificial Intelligence, and a standing committee member for the Organization of Service-Oriented Manufacturing. He is also the awardee of the General Research Fund (GRF) from Hong Kong RGC and the Science Fund for Young Scholars from the NSFC. More information about his research can be found on his personal website: //sites.google.com/view/shaochong

讲座内容摘要

In today’s ever-evolving business landscape, retailers have unprecedented access to vast amounts of data generated from their daily operations. This wealth of data presents unparalleled opportunities to predict uncertain demand and overcome decision challenges in inventory management. To harness the full potential of available data, machine learningand AI tools have emerged as indispensable techniques for driving high-quality, data-driven solutions. In this talk, I will delve into different data-drivencontextualoperations management settings(e.g., inventory management, pricing, and promotion optimization) that incorporate decision-bias effect, substitution effect, treatment effect, and confounding effect. We will discuss various data-driven decision-making frameworks seamlessly incorporate machine learningand AI elements, forming their foundation theories and offering valuable and practical insights from operational perspectives.