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Optimal discrete search with imperfect specificity

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Listed:
  • Moshe Kress
  • Kyle Lin
  • Roberto Szechtman

Abstract

A target is hidden in one of several possible locations, and the objective is to find the target as fast as possible. One common measure of effectiveness for the search process is the expected time of the search. This type of search optimization problem has been addressed and solved in the literature for the case where the searcher has imperfect sensitivity (possible false negative results), but perfect specificity (no false positive detections). In this paper, which is motivated by recent military and homeland security search situations, we extend the results to the case where the search is subject to false positive detections. Copyright Springer-Verlag 2008

Suggested Citation

  • Moshe Kress & Kyle Lin & Roberto Szechtman, 2008. "Optimal discrete search with imperfect specificity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(3), pages 539-549, December.
    • Handle: RePEc:spr:mathme:v:68:y:2008:i:3:p:539-549
    DOI: 10.1007/s00186-007-0197-2
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    References listed on IDEAS

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    1. John M. Danskin, 1962. "A Theory of Reconnaissance: I," Operations Research, INFORMS, vol. 10(3), pages 285-299, June.
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    4. Nah-Oak Song & Demosthenis Teneketzis, 2004. "Discrete search with multiple sensors," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 60(1), pages 1-13, September.
    5. Stephen M. Pollock, 1971. "Search Detection and Subsequent Action: Some Problems on the Interfaces," Operations Research, INFORMS, vol. 19(3), pages 559-586, June.
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    Citations

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    Cited by:

    1. Joseph Kadane, 2015. "Optimal discrete search with technological choice," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 317-336, June.
    2. Jake Clarkson & Kevin D. Glazebrook & Kyle Y. Lin, 2020. "Fast or Slow: Search in Discrete Locations with Two Search Modes," Operations Research, INFORMS, vol. 68(2), pages 552-571, March.
    3. Michael Atkinson & Moshe Kress & Rutger-Jan Lange, 2016. "When Is Information Sufficient for Action? Search with Unreliable yet Informative Intelligence," Operations Research, INFORMS, vol. 64(2), pages 315-328, April.
    4. T. C. E. Cheng & B. Kriheli & E. Levner & C. T. Ng, 2021. "Scheduling an autonomous robot searching for hidden targets," Annals of Operations Research, Springer, vol. 298(1), pages 95-109, March.
    5. Baycik, N. Orkun & Sharkey, Thomas C. & Rainwater, Chase E., 2020. "A Markov Decision Process approach for balancing intelligence and interdiction operations in city-level drug trafficking enforcement," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    6. Steven M. Shechter & Farhad Ghassemi & Yasin Gocgun & Martin L. Puterman, 2015. "Technical Note—Trading Off Quick versus Slow Actions in Optimal Search," Operations Research, INFORMS, vol. 63(2), pages 353-362, April.
    7. Joseph B. Kadane, 2015. "Optimal discrete search with technological choice," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 317-336, June.
    8. Kress, M. & Royset, J.O. & Rozen, N., 2012. "The eye and the fist: Optimizing search and interdiction," European Journal of Operational Research, Elsevier, vol. 220(2), pages 550-558.

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