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Resource Optimization in Mass Casualty Management: A Comparison of Methods

In: Operations Research Proceedings 2021

Author

Listed:
  • 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)

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.

Suggested Citation

  • Marian Sorin Nistor & Maximilian Moll & Truong Son Pham & Stefan Wolfgang Pickl & Dieter Budde, 2022. "Resource Optimization in Mass Casualty Management: A Comparison of Methods," Lecture Notes in Operations Research, in: Norbert Trautmann & Mario Gnägi (ed.), Operations Research Proceedings 2021, pages 415-420, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-08623-6_61
    DOI: 10.1007/978-3-031-08623-6_61
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