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A pseudo-polynomial heuristic for path-constrained discrete-time Markovian-target search

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  • Hong, Sung-Pil
  • Cho, Sung-Jin
  • Park, Myoung-Ju

Abstract

We propose a new heuristic for the single-searcher path-constrained discrete-time Markovian-target search. The algorithm minimizes an approximate, instead of exact, nondetection probability computed from the conditional probability that reflects the search history over the time windows of a fixed length, l. Having a pseudo-polynomial complexity, it can solve, in reasonable time, the instances an order of magnitude larger than those solved in the previous studies. By an asymptotic analysis relying on the fast-mixing Markov chain, we show that the relative error of the approximation exponentially diminishes as l increases and the experimental results confirm the analysis. The experiment also reveals a correlation very close to 1 between the approximate and exact nondetection probability of a search path. This means that the heuristic produces near-optimal search paths.

Suggested Citation

  • Hong, Sung-Pil & Cho, Sung-Jin & Park, Myoung-Ju, 2009. "A pseudo-polynomial heuristic for path-constrained discrete-time Markovian-target search," European Journal of Operational Research, Elsevier, vol. 193(2), pages 351-364, March.
    • Handle: RePEc:eee:ejores:v:193:y:2009:i:2:p:351-364
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    References listed on IDEAS

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    1. James N. Eagle & James R. Yee, 1990. "An Optimal Branch-and-Bound Procedure for the Constrained Path, Moving Target Search Problem," Operations Research, INFORMS, vol. 38(1), pages 110-114, February.
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    4. Scott Shorey Brown, 1980. "Optimal Search for a Moving Target in Discrete Time and Space," Operations Research, INFORMS, vol. 28(6), pages 1275-1289, December.
    5. James N. Eagle, 1984. "The Optimal Search for a Moving Target When the Search Path Is Constrained," Operations Research, INFORMS, vol. 32(5), pages 1107-1115, October.
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    Cited by:

    1. Adel Guitouni & Hatem Masri, 2014. "An orienteering model for the search and rescue problem," Computational Management Science, Springer, vol. 11(4), pages 459-473, October.

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