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Automated Support for Battle Operational–Strategic Decision-Making

Author

Listed:
  • Gerardo Minguela-Castro

    (Department of Computer Systems and Software Engineering, Universidad Nacional de Educacion a Distancia (UNED), 28040 Madrid, Spain)

  • Ruben Heradio

    (Department of Computer Systems and Software Engineering, Universidad Nacional de Educacion a Distancia (UNED), 28040 Madrid, Spain)

  • Carlos Cerrada

    (Department of Computer Systems and Software Engineering, Universidad Nacional de Educacion a Distancia (UNED), 28040 Madrid, Spain)

Abstract

Battle casualties are the subject of study in military operations research, which applies mathematical models to quantify the probability of victory vs. loss. In particular, different approaches have been proposed to model the course of battles. However, none of them provide adequate decision-making support for high-level command. To overcome this situation, this paper presents an innovative high-level decision-making model, which uses an adaptive and predictive control architecture. The paper reports empirical evidence supporting our model by considering one of the greatest battles of World War II: the Battle of Crete.

Suggested Citation

  • Gerardo Minguela-Castro & Ruben Heradio & Carlos Cerrada, 2021. "Automated Support for Battle Operational–Strategic Decision-Making," Mathematics, MDPI, vol. 9(13), pages 1-15, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1534-:d:585748
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    References listed on IDEAS

    as
    1. Duffey, Romney B, 2017. "Dynamic theory of losses in wars and conflicts," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1013-1027.
    2. Xavier Rubio-Campillo, 2016. "Model Selection in Historical Research Using Approximate Bayesian Computation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
    3. Jerome Bracken, 1995. "Lanchester models of the ardennes campaign," Naval Research Logistics (NRL), John Wiley & Sons, vol. 42(4), pages 559-577, June.
    4. S. J. Deitchman, 1962. "A Lanchester Model of Guerrilla Warfare," Operations Research, INFORMS, vol. 10(6), pages 818-827, December.
    5. Ronald D. Fricker, 1998. "Attrition models of the Ardennes campaign," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(1), pages 1-22, February.
    6. Thomas W. Lucas & Turker Turkes, 2004. "Fitting Lanchester equations to the battles of Kursk and Ardennes," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(1), pages 95-116, February.
    7. Kress, Moshe & Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Seidl, Andrea, 2018. "Lanchester model for three-way combat," European Journal of Operational Research, Elsevier, vol. 264(1), pages 46-54.
    8. Robert L. Helmbold, 1965. "Letter to the Editor—A Modification of Lanchester's Equations," Operations Research, INFORMS, vol. 13(5), pages 857-859, October.
    9. Donghyun Kim & Hyungil Moon & Donghyun Park & Hayong Shin, 2017. "An efficient approximate solution for stochastic Lanchester models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1470-1481, November.
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