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Network Optimization Models for Resource Allocation in Developing Military Countermeasures

Author

Listed:
  • Boaz Golany

    (Technion--Israel Institute of Technology, Haifa 32000, Israel)

  • Moshe Kress

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93940)

  • Michal Penn

    (Technion--Israel Institute of Technology, Haifa 32000, Israel)

  • Uriel G. Rothblum

    (Technion--Israel Institute of Technology, Haifa 32000, Israel)

Abstract

A military arms race is characterized by an iterative development of measures and countermeasures. An attacker attempts to introduce new weapons in order to gain some advantage, whereas a defender attempts to develop countermeasures that can mitigate or even eliminate the effects of the weapons. This paper addresses the defender's decision problem: given limited resources, which countermeasures should be developed and how much should be invested in their development to minimize the damage caused by the attacker's weapons over a certain time horizon. We formulate several optimization models, corresponding to different operational settings, as constrained shortest-path problems and variants thereof. We then demonstrate the potential applicability and robustness of this approach with respect to various scenarios.

Suggested Citation

  • Boaz Golany & Moshe Kress & Michal Penn & Uriel G. Rothblum, 2012. "Network Optimization Models for Resource Allocation in Developing Military Countermeasures," Operations Research, INFORMS, vol. 60(1), pages 48-63, February.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:1:p:48-63
    DOI: 10.1287/opre.1110.1002
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    References listed on IDEAS

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    1. Koubi, Vally, 1999. "Military Technology Races," International Organization, Cambridge University Press, vol. 53(3), pages 537-565, July.
    2. William M. Burnett & Dominic J. Monetta & Barry G. Silverman, 1993. "How the Gas Research Institute (GRI) Helped Transform the US Natural Gas Industry," Interfaces, INFORMS, vol. 23(1), pages 44-58, February.
    3. Refael Hassin, 1992. "Approximation Schemes for the Restricted Shortest Path Problem," Mathematics of Operations Research, INFORMS, vol. 17(1), pages 36-42, February.
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    Cited by:

    1. Chen, Shun & Zhao, Xudong & Chen, Zhilong & Hou, Benwei & Wu, Yipeng, 2022. "A game-theoretic method to optimize allocation of defensive resource to protect urban water treatment plants against physical attacks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 36(C).
    2. Zhang, Xiaoxiong & Ye, Yanqing & Tan, Yuejin, 2020. "How to protect a genuine target against an attacker trying to detect false targets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    3. Arthur Carvalho & Kate Larson, 2012. "Sharing Rewards Among Strangers Based on Peer Evaluations," Decision Analysis, INFORMS, vol. 9(3), pages 253-273, September.
    4. David Simchi-Levi & Nikolaos Trichakis & Peter Yun Zhang, 2019. "Designing Response Supply Chain Against Bioattacks," Operations Research, INFORMS, vol. 67(5), pages 1246-1268, September.
    5. Jian Xiong & Rui Wang & Jiang Jiang, 2019. "Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods," Complexity, Hindawi, vol. 2019, pages 1-18, November.
    6. Levitin, Gregory & Hausken, Kjell & Dai, Yuanshun, 2014. "Optimal defense with variable number of overarching and individual protections," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 81-90.

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