东南大学经济管理学院专题讨论（Seminar）登记表
报告题目 | Optimizing the regional trauma network via bi-level integer programming | ||
报告人（单位） | Nan Kong Associate Professor, Weldon School of Biomedical Engineering, Purdue University | ||
点评人（单位） | 王海燕（东南大学） | 点评人（单位） | |
时间地点 | 时间：2019年7月7日（周日）下午14:00 地点：九龙湖经管楼A402 | ||
报告内容摘要 | | ||
报告内容： Trauma continues to be the leading cause of mortality and morbidity among US citizens younger than 45 years of age. Evidence suggests that mal-distribution of high-level trauma centers and potentially too many low-level trauma centers and/or regular hospitals close to popular incident scenes can significantly contribute to pre-hospital mistriage errors (both under and over). Under-triage, transporting severely injured patients to a regular hospital, can lead to various health risks and even mortality. Over-triage, transporting less-severely injured patients to a hospital specializing in trauma care, leads to inappropriate use of trauma care resources, and higher healthcare spending, especially of publically funded patients. Although the government (local or state) has little or no direct authority in promoting the location of trauma centers, they can influence the hospital system(s) who own these centers by offering financial subsidies. In this talk, we consider the problem of optimizing a regional trauma network that minimizes the negative effects of UT (to improving social well-being) and OT (to reduce spending) errors in the presence of two decision-makers, the government and a hospital system. We present a novel bi-level subsidized network redesign problem, in which the government’s (upper-level) decision is to determine the total subsidy to support social well-being and minimize public spending; whereas the hospital system’s (lower-level) decision is to upgrade/downgrade facility status to maximize its revenue. We design a branch-and-bound based algorithm for the resultant bi-level integer programming model with an improved branching rule. Through comprehensive numerical experiments with randomly generated instances, we are able to show the superiority of our algorithm in comparison with state-of-the-art algorithms for bi-level integer programming. At the end of the talk, we will present case studies based on real incidence, geography, and cost data from the state of Ohio.
报告人简介： Professor Nan Kong is Associate Professor in the Weldon School of Biomedical Engineering at Purdue University. He is also on the Faculty Advisory Team for Purdue’s Regenstrief Center for Healthcare Engineering. He received his PhD in industrial engineering from the University of Pittsburgh in 2006. His research interest includes healthcare operations management, in particular healthcare network design, provider staff scheduling, and within-network patient flow control. Recently, he has expanded his research to machine learning for biomedicine. He has published over 50 peer-reviewed journal articles. He is an Associate Editor for the IIE Transactions on Healthcare Systems Engineering and for the International Conference on Automation Science and Engineering. His research has been or currently being funded by the National Science Foundation, National Cancer Institute, Agency for Health Research and Quality, Centers for Medicare and Medicaid Services, and Air Force Office of Scientific Research. He is currently President of the Public Sector OR Section in INFORMS and Committee Chair for the INFORMS Undergraduate Operations Research Prize. | |||
