A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events

Catastrophic health events are natural or man-made incidents that create casualties in numbers that overwhelm the response capabilities of healthcare systems. Proper response planning for such events requires community-based surge solutions such as the location of alternative care facilities and way...

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Bibliographic Details
Published in:Transportation science 2018-03, Vol.52 (2), p.437-453
Main Authors: Caunhye, Aakil M, Nie, Xiaofeng
Format: Article
Language:English
Subjects:
alternative care facilities
Alternative medicine
Catastrophic events
catastrophic health events
cuts
Disasters
Emergency management
Emergency preparedness
Health aspects
Health care management
Mathematical research
Stochastic models
Stochastic programming
triage
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Summary:Catastrophic health events are natural or man-made incidents that create casualties in numbers that overwhelm the response capabilities of healthcare systems. Proper response planning for such events requires community-based surge solutions such as the location of alternative care facilities and ways to improve coordination by considering triage and the movement of self-evacuees. In this paper, we construct a three-stage stochastic programming model to locate alternative care facilities and allocate casualties in response to catastrophic health events. Our model integrates casualty triage and the movement of self-evacuees in a systemic response framework that treats uncertainties involved in such large-scale events as probabilistically distributed scenarios. Solution times being instrumental to the practicality of the model, we propose an algorithm, based on Benders decomposition, to generate good solutions fast. We derive new valid inequalities, which we add to the Benders decomposition master problem to reduce the number of weak feasibility cuts generated. Because our algorithm can also be ineffective if the number of scenarios is large, we propose a two-stage approximation model that attempts to guess good third-stage solutions without third-stage decision variables and constraints. Our model, algorithm, and two-stage approximation are implemented in the case study of an earthquake situation in California based on the realistic ShakeOut Scenario data. The online appendix is available at https://doi.org/10.1287/trsc.2017.0777 .
ISSN:0041-1655
1526-5447
DOI:10.1287/trsc.2017.0777