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Path optimization for the resource‐constrained searcher

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  • Hiroyuki Sato
  • Johannes O. Royset

Abstract

We formulate and solve a discrete‐time path‐optimization problem where a single searcher, operating in a discretized three‐dimensional airspace, looks for a moving target in a finite set of cells. The searcher is constrained by maximum limits on the consumption of one or more resources such as time, fuel, and risk along any path. We develop a specialized branch‐and‐bound algorithm for this problem that uses several network reduction procedures as well as a new bounding technique based on Lagrangian relaxation and network expansion. The resulting algorithm outperforms a state‐of‐the‐art algorithm for solving time‐constrained problems and also is the first algorithm to solve multi‐constrained problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010

Suggested Citation

  • 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.
    • Handle: RePEc:wly:navres:v:57:y:2010:i:5:p:422-440
    DOI: 10.1002/nav.20411
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    References listed on IDEAS

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    1. Dean D. Dewolfe & James G. Stevens & R. Kevin Wood, 1993. "Setting military reenlistment bonuses," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(2), pages 143-160, March.
    2. 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.
    3. Alan R. Washburn, 1983. "Search for a Moving Target: The FAB Algorithm," Operations Research, INFORMS, vol. 31(4), pages 739-751, August.
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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. 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).
    4. 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.
    5. Jesse Pietz & Johannes O. Royset, 2013. "Generalized orienteering problem with resource dependent rewards," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(4), pages 294-312, June.
    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.

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