时间：June 10, 2025, 15:00-17:00       

地点：中关村主楼216

报告人：何龙

报告人国籍：中国

报告人政治面貌：群众

报告人职称：Associate Professor

报告人工作单位：George Washington University

报告人简介：

Long He is an associate professor of decision sciences at the School of Business, George Washington University. Prior to joining GW, Long was an associate professor in the Department of Analytics & Operations at NUS Business School, National University of Singapore. He received his Ph.D. in Operations Research from the University of California, Berkeley, and his B.Eng. in Logistics Management and Engineering from HKUST. His current research involves using data-driven approaches to address problems in smart city operations (e.g., vehicle sharing, last-mile delivery) and supply chain management. This line of research has been recognized with the M&SOM Journal Best Paper Award, Transportation Science & Logistics (TSL) Best Paper Award, and ENRE Best Publication Award in Energy from INFORMS.

报告内容简介：

This paper addresses police resource allocation across multiple locations, aiming to minimize the overall cost of potential crimes. Unlike previous literature focused on reactive police tasks, we propose a proactive approach that emphasizes crime prevention through deterrence. To account for the deterrence effect of police resources on crime, we employ the multinomial logit model to calibrate the distribution of crime locations. Our model sheds light on two facets of the deterrence effect in proactive policing—crime control diffusion and crime displacement—relevant to modern crime patterns from both criminology and economics perspectives. We also investigate the structural properties of our problem and its relation to mixture-of-logits assortment optimization. Additionally, we provide reformulations for mixed-integer linear/conic programs that can be solved directly using conventional optimization software. Finally, we showcase the efficacy of our model through a data-driven case study on the allocation of surveillance cameras in New York City..

（承办：管理工程系、科研与学术交流中心）