講 題:Optimal decision making of a mass casualty incident in disaster responses
主 講 人:陳子立 副教授 清華大學工業工程與工程管理學系
主辦單位:陽明交通大學工業工程與管理系
時 間:114年12月8日(星期一) 13:20 ~ 15:20
地 點:管理二館MB520教室
演講摘要:After a severe disaster strikes, a large number of casualties, many severe, arise from a single disaster area or multiple disaster areas in a very short period of time. This event, called an MCI, not only disrupts the normal functioning of emergency department services of the hospital but also overwhelms available resources (e.g., personnel, bed, and ambulance) of the local healthcare system and emergency medical services. Therefore, efficient, critical mass casualty management (MCM) can substantially increase the survival probability of the casualties in the response phase of a disaster. MCM encompasses a series of decision-making processes from the strategic, tactical, and operational levels. Strategic decisions made during the pre-disaster stage determine the pre-positioning of the candidate casualty collection points (CCPs) such as shelters, schools, parking lots, hospitals etc. Tactical decisions made at the beginning of post-disaster response phase determine the opening decision from the candidate CCPs and the allocation decision of scarce emergency medical resources (EMS) including ambulances, firefighters, and medical personnel to quickly mitigate the severity of potential threats. To minimize the number of deaths and maximize the number of survivors, the following operational decisions are dynamically made over time given CCP locations and EMS resource: (1) the patient prioritization determines the order of casualties transported to the hospital; (2) the hospital selection transfers severe casualties to appropriate hospitals by ambulances; and (3) the ambulance redeployment re-dispatches the ambulances which have completed service to the CCPs in need. In this talk, we will investigates tactical and operational decisions in the post-disaster stage using stochastic optimization approaches such as simulation optimization, stochastic programming and approximate dynamic programming. By collaborating with National Science and Technology Center for Disaster Reduction (NCDR) and obtaining an earthquake scenario occurring in Tainan City, Taiwan, the computational efficiency of the proposed stochastic optimization model and algorithm is demonstrated and verified.
演講性質:學術研究專題
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