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Generalized orienteering problem with resource dependent rewards

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  • Jesse Pietz
  • Johannes O. Royset

Abstract

We introduce a generalized orienteering problem (OP) where, as usual, a vehicle is routed from a prescribed start node, through a directed network, to a prescribed destination node, collecting rewards at each node visited, to maximize the total reward along the path. In our generalization, transit on arcs in the network and reward collection at nodes both consume a variable amount of the same limited resource. We exploit this resource trade‐off through a specialized branch‐and‐bound algorithm that relies on partial path relaxation problems that often yield tight bounds and lead to substantial pruning in the enumeration tree. We present the smuggler search problem (SSP) as an important real‐world application of our generalized OP. Numerical results show that our algorithm applied to the SSP outperforms standard mixed‐integer nonlinear programming solvers for moderate to large problem instances. We demonstrate model enhancements that allow practitioners to represent realistic search planning scenarios by accounting for multiple heterogeneous searchers and complex smuggler motion. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013

Suggested Citation

  • 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.
  • Handle: RePEc:wly:navres:v:60:y:2013:i:4:p:294-312
    DOI: 10.1002/nav.21534
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    Cited by:

    1. Yu, Qinxiao & Fang, Kan & Zhu, Ning & Ma, Shoufeng, 2019. "A matheuristic approach to the orienteering problem with service time dependent profits," European Journal of Operational Research, Elsevier, vol. 273(2), pages 488-503.
    2. Kim, Hyunjoon & Kim, Byung-In, 2022. "Hybrid dynamic programming with bounding algorithm for the multi-profit orienteering problem," European Journal of Operational Research, Elsevier, vol. 303(2), pages 550-566.
    3. Bijun Wang & Zheyong Bian & Mo Mansouri, 2023. "Self-adaptive heuristic algorithms for the dynamic and stochastic orienteering problem in autonomous transportation system," Journal of Heuristics, Springer, vol. 29(1), pages 77-137, February.
    4. Ksenia D Mukhina & Alexander A Visheratin & Denis Nasonov, 2019. "Orienteering Problem with Functional Profits for multi-source dynamic path construction," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-15, April.
    5. Qinxiao Yu & Yossiri Adulyasak & Louis-Martin Rousseau & Ning Zhu & Shoufeng Ma, 2022. "Team Orienteering with Time-Varying Profit," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 262-280, January.
    6. Yan Xia & Rajan Batta & Rakesh Nagi, 2017. "Controlling a Fleet of Unmanned Aerial Vehicles to Collect Uncertain Information in a Threat Environment," Operations Research, INFORMS, vol. 65(3), pages 674-692, June.

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