Resource Optimization in Mass Casualty Management: A Comparison of Methods

Marian Sorin Nistor (), Maximilian Moll, Truong Son Pham, Stefan Wolfgang Pickl and Dieter Budde
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Marian Sorin Nistor: Universität der Bundeswehr München
Maximilian Moll: Universität der Bundeswehr München
Truong Son Pham: Universität der Bundeswehr München
Stefan Wolfgang Pickl: Universität der Bundeswehr München
Dieter Budde: Universität der Bundeswehr München

A chapter in Operations Research Proceedings 2021, 2022, pp 415-420 from Springer

Abstract: Abstract This paper studies and compares various optimization approaches ranging from classical optimization to machine learning to respond swiftly and optimally in casualty incidents. Key points of interest in the comparison are the solution quality and the speed of finding it. In multiple-casualty scenarios knowing both is essential to choosing the correct method. A set of 960 synthetic MCI scenarios of different settings are being considered here to give an indication of scalability. For these scenarios, the aim is to optimize the number of victims receiving specialized treatments at the nearest available hospital.

Keywords: Disaster and crisis management; Mass casualty incidents; Optimization; Casualty processing schedule (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-031-08623-6_61

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