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An iterative three-component heuristic for the team orienteering problem with time windows

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  • Hu, Qian
  • Lim, Andrew

Abstract

This paper studies the team orienteering problem with time windows, the aim of which is to maximize the total profit collected by visiting a set of customers with a limited number of vehicles. Each customer has a profit, a service time and a time window. A service provided to any customer must begin in his or her time window. We propose an iterative framework incorporating three components to solve this problem. The first two components are a local search procedure and a simulated annealing procedure. They explore the solution space and discover a set of routes. The third component recombines the routes to identify high quality solutions. Our computational results indicate that this heuristic outperforms the existing approaches in the literature in average performance by at least 0.41%. In addition, 35 new best solutions are found.

Suggested Citation

  • Hu, Qian & Lim, Andrew, 2014. "An iterative three-component heuristic for the team orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 232(2), pages 276-286.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:2:p:276-286
    DOI: 10.1016/j.ejor.2013.06.011
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    2. 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.
    3. Zhao, Yanlu & Alfandari, Laurent, 2020. "Design of diversified package tours for the digital travel industry : A branch-cut-and-price approach," European Journal of Operational Research, Elsevier, vol. 285(3), pages 825-843.
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    5. Damianos Gavalas & Charalampos Konstantopoulos & Konstantinos Mastakas & Grammati Pantziou, 2019. "Efficient Cluster-Based Heuristics for the Team Orienteering Problem with Time Windows," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-44, February.
    6. Kirac, Emre & Milburn, Ashlea Bennett, 2018. "A general framework for assessing the value of social data for disaster response logistics planning," European Journal of Operational Research, Elsevier, vol. 269(2), pages 486-500.
    7. Lee, Alan & Savelsbergh, Martin, 2015. "Dynamic ridesharing: Is there a role for dedicated drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 483-497.
    8. Oktay Yılmaz & Ertan Yakıcı & Mumtaz Karatas, 2019. "A UAV location and routing problem with spatio-temporal synchronization constraints solved by ant colony optimization," Journal of Heuristics, Springer, vol. 25(4), pages 673-701, October.
    9. Lei, Chao & Lin, Wei-Hua & Miao, Lixin, 2014. "A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem," European Journal of Operational Research, Elsevier, vol. 238(3), pages 699-710.
    10. Gunawan, Aldy & Lau, Hoong Chuin & Vansteenwegen, Pieter, 2016. "Orienteering Problem: A survey of recent variants, solution approaches and applications," European Journal of Operational Research, Elsevier, vol. 255(2), pages 315-332.
    11. Zhang, Shu & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2020. "Multi-period orienteering with uncertain adoption likelihood and waiting at customers," European Journal of Operational Research, Elsevier, vol. 282(1), pages 288-303.
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