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A model for geographically distributed combat interactions of swarming naval and air forces

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  • Connor McLemore
  • Donald Gaver
  • Patricia Jacobs

Abstract

This article describes the Distributed Interaction Campaign Model (DICM), an exploratory campaign analysis tool and asset allocation decision‐aid for managing geographically distributed and swarming naval and air forces. The model is capable of fast operation, while accounting for uncertainty in an opponent's plan. It is intended for use by commanders and analysts who have limited time for model runs, or a finite budget. The model is purpose‐built for the Pentagon's Office of Net Assessment, and supports analysis of the following questions: What happens when swarms of geographically distributed naval and air forces engage each other and what are the key elements of the opponents’ force to attack? Are there changes to force structure that make a force more effective, and what impacts will disruptions in enemy command and control and wide‐area surveillance have? Which insights are to be gained by fast exploratory mathematical/computational campaign analysis to augment and replace expensive and time‐consuming simulations? An illustrative example of model use is described in a simple test scenario. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 562–576, 2016

Suggested Citation

  • Connor McLemore & Donald Gaver & Patricia Jacobs, 2016. "A model for geographically distributed combat interactions of swarming naval and air forces," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(7), pages 562-576, October.
    • Handle: RePEc:wly:navres:v:63:y:2016:i:7:p:562-576
    DOI: 10.1002/nav.21720
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    References listed on IDEAS

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    1. González, Eduardo & Villena, Marcelo, 2011. "Spatial Lanchester models," European Journal of Operational Research, Elsevier, vol. 210(3), pages 706-715, May.
    2. P.S. Sheeba & Debasish Ghose, 2008. "Optimal resource allocation and redistribution strategy in military conflicts with Lanchester square law attrition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(6), pages 581-591, September.
    3. Debasish Ghose & Jason Speyer & Jeff Shamma, 2002. "A Game Theoretical Multiple Resource Interaction Approach to Resource Allocation in an Air Campaign," Annals of Operations Research, Springer, vol. 109(1), pages 15-40, January.
    4. Timothy C. Barkdoll & Donald P. Gaver & Kevin D. Glazebrook & Patricia A. Jacobs & Sergio Posadas, 2002. "Suppression of enemy air defenses (SEAD) as an information duel," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(8), pages 723-742, December.
    5. Harrison C. Schramm & Donald P. Gaver, 2013. "Lanchester for cyber: The mixed epidemic‐combat model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(7), pages 599-605, October.
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    Cited by:

    1. 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.

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