报告时间：2024年7月13日（星期六）上午10点

报告地点：工程管理与智能制造大楼1425会议室

报告人：张宏哲助理教授

工作单位：香港中文大学（深圳）

举办单位：管理学院

报告简介：

Disaster response is critical to save lives and reduce damages in the aftermath of a disaster. Fundamental to disaster response operations is the management of disaster relief resources. To this end, a local agency (e.g., a local emergency resource distribution center) collects demands from local communities affected by a disaster, dispatches available resources to meet the demands, and requests more resources from a central emergency management agency (e.g., Federal Emergency Management Agency or FEMA in the U.S.). Prior resource management research for disaster response overlooks the problem of deciding optimal quantities of resources requested by a local agency. In response to this research gap, we formulate a new resource management problem that proactively decides optimal quantities of requested resources by considering both currently unfulfilled demands and future demands. To solve the problem, we take salient characteristics of the problem into consideration and develop a novel deep learning method for future demand prediction. We then formulate the problem as a stochastic optimization model, analyze key properties of the model, and propose an effective solution method to the problem based on the analyzed properties. We demonstrate the superior performance of our method over prevalent existing methods using both real world and simulated data. We also show its superiority over prevalent existing methods in a multi-stakeholder and multi-objective setting through simulations.

报告人简介：

张宏哲现为香港中文大学（深圳）信息系统方向助理教授。于美国特拉华大学商学院取得了金融服务分析的博士学位。其研究聚焦于应用与改进计算机科学（如机器学习）和管理科学（如优化）等领域的方法和工具，解决金融科技、隐私保护人工智能和社会管理中的重要问题。研究成果发表于知名学术期刊及学术会议，如Information Systems Research，ICDE等。