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A Stochastic Program for Interdicting Smuggled Nuclear Material

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Network Interdiction and Stochastic Integer Programming

Part of the book series: Operations Research/Computer Science Interfaces Series ((ORCS,volume 22))

Abstract

This paper describes a stochastic network interdiction model for identifying locations for installing detectors sensitive to nuclear material. A nuclear material smuggler selects a path through a transportation network that maximizes the probability of avoiding detection An interdictor installs sensors to minimize that maximum probability. This problem is formulated as a bi-level stochastic mixed-integer program. The program is stochastic because the evader’s origin and destination are unknown at the time the detectors are installed. The model is reformulated as a two-stage stochastic mixed-integer program with recourse and is shown to be strongly NP-Hard. We describe an application of our model to help strengthen the overall capability of preventing the illicit trafficking of nuclear materials.

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Author information

Authors and Affiliations

  1. Graduate Program in Operations Research Mechanical Engineering Department, The University of Texas at Austin, Austin, TX, 78712, USA

    Feng Pan & David P. Morton

  2. Nuclear Engineering Teaching Laboratory Mechanical Engineering Department, The University of Texas at Austin, Austin, TX, 78758, USA

    William S. Charlton

Authors
  1. Feng Pan
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  2. William S. Charlton
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  3. David P. Morton
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Editor information

Editors and Affiliations

  1. University of California, Davis

    David L. Woodruff

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© 2003 Kluwer Academic Publishers

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Pan, F., Charlton, W.S., Morton, D.P. (2003). A Stochastic Program for Interdicting Smuggled Nuclear Material. In: Woodruff, D.L. (eds) Network Interdiction and Stochastic Integer Programming. Operations Research/Computer Science Interfaces Series, vol 22. Springer, Boston, MA. https://doi.org/10.1007/0-306-48109-X_1

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  • DOI: https://doi.org/10.1007/0-306-48109-X_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7302-1

  • Online ISBN: 978-0-306-48109-3

  • eBook Packages: Springer Book Archive

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