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Interdiction Models and Applications

In: Handbook of Operations Research for Homeland Security

Author

Listed:
  • Nedialko B. Dimitrov

    (Naval Postgraduate School)

  • David P. Morton

    (The University of Texas at Austin)

Abstract

Through interdiction models, we infer the vulnerabilities inherent in an operational system. This chapter presents four applications of interdiction modeling: (a) to delay an adversary’s development of a first nuclear weapon; (b) to understand vulnerabilities in an electric power system; (c) to locate sensors in a municipal water network; and (d) to secure a border against a nuclear smuggler. In each case, we detail and interpret the mathematical model and characterize insights gained from solving instances of the model. We point to special structures that sometimes arise in interdiction models and the associated implications for analyses. From these examples, themes emerge on how one should model, and defend against, an intelligent adversary.

Suggested Citation

  • Nedialko B. Dimitrov & David P. Morton, 2013. "Interdiction Models and Applications," International Series in Operations Research & Management Science, in: Jeffrey W. Herrmann (ed.), Handbook of Operations Research for Homeland Security, edition 127, chapter 0, pages 73-103, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-5278-2_4
    DOI: 10.1007/978-1-4614-5278-2_4
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    Citations

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    Cited by:

    1. Ketkov, Sergey S. & Prokopyev, Oleg A., 2020. "On greedy and strategic evaders in sequential interdiction settings with incomplete information," Omega, Elsevier, vol. 92(C).
    2. Yongjia Song & Siqian Shen, 2016. "Risk-Averse Shortest Path Interdiction," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 527-539, August.
    3. Eghbal Rashidi & Hugh Medal & Aaron Hoskins, 2018. "An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(2), pages 120-134, March.
    4. David L. Alderson & Gerald G. Brown & W. Matthew Carlyle & R. Kevin Wood, 2018. "Assessing and Improving the Operational Resilience of a Large Highway Infrastructure System to Worst-Case Losses," Transportation Science, INFORMS, vol. 52(4), pages 1012-1034, August.
    5. Stefano Berrone & Francesco Della Santa & Antonio Mastropietro & Sandra Pieraccini & Francesco Vaccarino, 2022. "Graph-Informed Neural Networks for Regressions on Graph-Structured Data," Mathematics, MDPI, vol. 10(5), pages 1-29, March.

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