Aeromedical Battlefield Evacuation Under Endogenous Uncertainty in Casualty Delivery Times

We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely evacuation and medical treatment to injured soldiers. The model enforces the “Golden Hour” evacuation doctrine, attempts to maximize the expected number of se...

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Veröffentlicht in:Management science Jg. 64; H. 12; S. 5481 - 5496
Hauptverfasser: Lejeune, Miguel A., Margot, Francois
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Linthicum INFORMS 01.12.2018
Institute for Operations Research and the Management Sciences
Schlagworte:
Afghanistan War
Airplane ambulances
Algorithms
Armed forces
Branch & bound algorithms
Casualties
Deaths
Endogenous
endogenous uncertainty
Evacuation
Evacuations & rescues
Intelligence
Management
MEDEVAC
Medical treatment
Military aspects
Military personnel
military trauma care
mixed-integer nonlinear programming
Nonlinear programming
Relaxation
relaxation and restriction problems
Robustness
Soldiers
spatial branch-and-boundalgorithm
Supply and demand
Technology application
Transport of sick and wounded
Uncertainty
United States
Afghanistan
ISSN:0025-1909, 1526-5501
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Zusammenfassung:We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely evacuation and medical treatment to injured soldiers. The model enforces the “Golden Hour” evacuation doctrine, attempts to maximize the expected number of severely injured soldiers evacuated within one hour without delay, and represents the availability of air ambulances as an endogenous source of uncertainty. The MEDEVAC model is a mixed-integer nonlinear programming problem whose continuous relaxation is in general nonconvex and for which we develop an algorithmic method articulated around (i) new bounding techniques obtained through the solution of restriction and relaxation problems and (ii) a spatial branch-and-bound algorithm solving conic mixed-integer programs at each node. The computational study, based on data from Operation Enduring Freedom, reveals that the bounding problems can be quickly solved regardless of problem size, the bounds are tight, and the spatial branch-and-bound dominates the CPLEX and BARON solvers in terms of computational time and robustness. Compared to the MEDEVAC myopic policy, our approach increases the number of casualties treated timely and can contribute to reducing the number of deaths on the battlefield. The benefits increase as the MEDEVAC resources become tighter and the combats intensify. The model can be used at the strategic level to design an efficient MEDEVAC system and at the tactical level for intelligent tasking and dispatching. Data are available at https://doi.org/10.1287/mnsc.2017.2894 . This paper was accepted by Yinyu Ye, optimization.
Bibliographie:ObjectType-Article-1
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ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.2017.2894