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A salvo model of warships in missile combat used to evaluate their staying power

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  • Wayne P. Hughes

Abstract

A methodology is introduced with which to compare the military worth of warship capabilities. It is based on a simple salvo model for exploratory analysis of modern combat characteristics. The “fractional exchange ratio” is suggested as a robust way to compare equal‐cost configurations of naval forces, because we cannot know in advance how and where the warships will fight. To aid in exposition, definitions of all terms are included in Appendix A. The methodology is illustrated with important conclusions from parametric analysis, among which are 1 Unstable circumstances arise as the combat power of the forces grows relative to their survivability. (Stable means the persistence of victory by the side with the greater combat potential.) 2 Weak staying power is likely to be the root cause when instability is observed. 3 Staying power is the ship design element least affected by the particulars of a battle, including poor tactics. 4 Numerical superiority is the force attribute that is consistently most advantageous. For example, if A's unit striking power, staying power, and defensive power are all twice that of B, nevertheless B will achieve parity of outcome if it has twice as many units as A. © 1995 John Wiley & Sons, Inc. This article is a US Government work and, as such, is in the public domain in the United States of America.

Suggested Citation

  • Wayne P. Hughes, 1995. "A salvo model of warships in missile combat used to evaluate their staying power," Naval Research Logistics (NRL), John Wiley & Sons, vol. 42(2), pages 267-289, March.
  • Handle: RePEc:wly:navres:v:42:y:1995:i:2:p:267-289
    DOI: 10.1002/1520-6750(199503)42:23.0.CO;2-Y
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    References listed on IDEAS

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    1. James G. Taylor & Gerald G. Brown, 1983. "Annihilation Prediction for Lanchester-Type Models of Modern Warfare," Operations Research, INFORMS, vol. 31(4), pages 752-771, August.
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    1. Kolebaje, Olusola & Popoola, Oyebola & Khan, Muhammad Altaf & Oyewande, Oluwole, 2020. "An epidemiological approach to insurgent population modeling with the Atangana–Baleanu fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Michael J. Armstrong, 2007. "Effective attacks in the salvo combat model: Salvo sizes and quantities of targets," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(1), pages 66-77, February.
    3. 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.
    4. Thomas W. Lucas & John E. McGunnigle, 2003. "When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(3), pages 197-217, April.
    5. Chad W. Seagren & Donald P. Gaver & Patricia A. Jacobs, 2019. "A stochastic air combat logistics decision model for Blue versus Red opposition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 663-674, December.
    6. Anelí Bongers & José L. Torres, 2021. "A bottleneck combat model: an application to the Battle of Thermopylae," Operational Research, Springer, vol. 21(4), pages 2859-2877, December.
    7. Michael J. Armstrong, 2013. "The salvo combat model with area fire," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(8), pages 652-660, December.
    8. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    9. Michael J. Armstrong, 2014. "Modeling Short-Range Ballistic Missile Defense and Israel's Iron Dome System," Operations Research, INFORMS, vol. 62(5), pages 1028-1039, October.
    10. Anelí Bongers & José L. Torres, 2017. "Revisiting the Battle of Midway: A counterfactual analysis," Working Papers 2017-01, Universidad de Málaga, Department of Economic Theory, Málaga Economic Theory Research Center.
    11. Hans Liwång, 2020. "The interconnectedness between efforts to reduce the risk related to accidents and attacks - naval examples," Journal of Transportation Security, Springer, vol. 13(3), pages 245-272, December.
    12. Michael J. Armstrong, 2005. "A Stochastic Salvo Model for Naval Surface Combat," Operations Research, INFORMS, vol. 53(5), pages 830-841, October.
    13. Michael J. Armstrong, 2004. "Effects of lethality in naval combat models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(1), pages 28-43, February.
    14. Chen Wang & Vicki M. Bier, 2016. "Quantifying Adversary Capabilities to Inform Defensive Resource Allocation," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 756-775, April.
    15. Claire Walton & Panos Lambrianides & Isaac Kaminer & Johannes Royset & Qi Gong, 2018. "Optimal motion planning in rapid‐fire combat situations with attacker uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(2), pages 101-119, March.
    16. Michael Armstrong, 2011. "A verification study of the stochastic salvo combat model," Annals of Operations Research, Springer, vol. 186(1), pages 23-38, June.
    17. Younglak Shim & Michael P. Atkinson, 2018. "Analysis of artillery shoot‐and‐scoot tactics," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(3), pages 242-274, April.

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