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Discounted MEAN bound for the optimal searcher path problem with non-uniform travel times

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  • Lau, Haye
  • Huang, Shoudong
  • Dissanayake, Gamini

Abstract

We consider an extension of the optimal searcher path problem (OSP), where a searcher moving through a discretised environment may now need to spend a non-uniform amount of time travelling from one region to another before being able to search it for the presence of a moving target. In constraining not only where but when the search of each cell can take place, the problem more appropriately models the search of environments which cannot be easily partitioned into equally sized cells. An existing OSP bounding method in literature, the MEAN bound, is generalised to provide bounds for solving the new problem in a branch and bound framework. The main contribution of this paper is an enhancement, discounted MEAN (DMEAN), which greatly tightens the bound for the new and existing problems alike with almost no additional computation. We test the new algorithm against existing OSP bounding methods and show it leads to faster solution times for moving target search problems.

Suggested Citation

  • Lau, Haye & Huang, Shoudong & Dissanayake, Gamini, 2008. "Discounted MEAN bound for the optimal searcher path problem with non-uniform travel times," European Journal of Operational Research, Elsevier, vol. 190(2), pages 383-397, October.
    • Handle: RePEc:eee:ejores:v:190:y:2008:i:2:p:383-397
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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.
    2. Hohzaki, Ryusuke & Iida, Koji, 1997. "Optimal strategy of route and look for the path constrained search problem with reward criterion," European Journal of Operational Research, Elsevier, vol. 100(1), pages 236-249, July.
    3. Udo Lössner & Ingo Wegener, 1982. "Discrete Sequential Search with Positive Switch Cost," Mathematics of Operations Research, INFORMS, vol. 7(3), pages 426-440, August.
    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.
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    Cited by:

    1. Johannes O. Royset & Hiroyuki Sato, 2010. "Route optimization for multiple searchers," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(8), pages 701-717, December.
    2. Ron Teller & Moshe Zofi & Moshe Kaspi, 2019. "Minimizing the average searching time for an object within a graph," Computational Optimization and Applications, Springer, vol. 74(2), pages 517-545, November.
    3. Bourque, François-Alex, 2019. "Solving the moving target search problem using indistinguishable searchers," European Journal of Operational Research, Elsevier, vol. 275(1), pages 45-52.
    4. Hiroyuki Sato & Johannes O. Royset, 2010. "Path optimization for the resource‐constrained searcher," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 422-440, August.
    5. Manon Raap & Silja Meyer-Nieberg & Stefan Pickl & Martin Zsifkovits, 2017. "Aerial Vehicle Search-Path Optimization: A Novel Method for Emergency Operations," Journal of Optimization Theory and Applications, Springer, vol. 172(3), pages 965-983, March.
    6. Morin, Michael & Abi-Zeid, Irène & Quimper, Claude-Guy, 2023. "Ant colony optimization for path planning in search and rescue operations," European Journal of Operational Research, Elsevier, vol. 305(1), pages 53-63.
    7. 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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