Optimizing emergency preparedness and resource utilization in mass-casualty incidents

This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times

Distribution-Free Model for Ambulance Location Problem with Ambiguous Demand

Ambulance location problem is a key issue in Emergency Medical Service (EMS) system, which is to determine where to locate ambulances such that the emergency calls can be responded efficiently. Most related researches focus on deterministic problems or assume that the probability distribution of demand can be estimated. In practice, however, it is difficult to obtain perfect information on probability distribution. This paper investigates the ambulance location problem with partial demand information; i.e., only the mean and covariance matrix of the demands are known. The problem consists of determining base locations and the employment of ambulances, to minimize the total cost. A new distribution-free chance constrained model is proposed. Then two approximated mixed integer programming (MIP) formulations are developed to solve it. Finally, numerical experiments on benchmarks (Nickel et al., 2016) and 120 randomly generated instances are conducted, and computational results show that our proposed two formulations can ensure a high service level in a short time. Specifically, the second formulation takes less cost while guaranteeing an appropriate service level. Document type: Articl

A STOCHASTIC PROGRAMMING APPROACH TO ANALYZE DESIGN AND MANAGEMENT OF FLEXIBILITY IN INFRASTRUCTURE SYSTEMS OPERATING UNDER LONG-TERM UNCERTAINTY

Ph.DDOCTOR OF PHILOSOPH

Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

This dissertation considers the importance of optimizing deployed military medical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For optimization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov decision processes, approximate dynamic programming, and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations