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Pareto mimic algorithm: An approach to the team orienteering problem

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  • Ke, Liangjun
  • Zhai, Laipeng
  • Li, Jing
  • Chan, Felix T.S.

Abstract

The team orienteering problem is an important variant of the vehicle routing problem. In this paper, a new algorithm, called Pareto mimic algorithm, is proposed to deal with it. This algorithm maintains a population of incumbent solutions which are updated using Pareto dominance. It uses a new operator, called mimic operator, to generate a new solution by imitating an incumbent solution. Furthermore, to improve the quality of a solution, it employs an operator, called swallow operator which attempts to swallow (or insert) an infeasible node and then repair the resulting infeasible solution. A comparative study supports the effectiveness of the proposed algorithm, especially, our algorithm can quickly find new better results for several large-scale instances. We also demonstrate that Pareto mimic algorithm can be generalized to solve other routing problems, e.g., the capacitated vehicle routing problem.

Suggested Citation

  • Ke, Liangjun & Zhai, Laipeng & Li, Jing & Chan, Felix T.S., 2016. "Pareto mimic algorithm: An approach to the team orienteering problem," Omega, Elsevier, vol. 61(C), pages 155-166.
    • Handle: RePEc:eee:jomega:v:61:y:2016:i:c:p:155-166
    DOI: 10.1016/j.omega.2015.08.003
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    5. 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.
    6. 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.
    7. Alejandro Estrada-Moreno & Albert Ferrer & Angel A. Juan & Javier Panadero & Adil Bagirov, 2020. "The Non-Smooth and Bi-Objective Team Orienteering Problem with Soft Constraints," Mathematics, MDPI, vol. 8(9), pages 1-16, September.
    8. Erika M. Herrera & Javier Panadero & Patricia Carracedo & Angel A. Juan & Elena Perez-Bernabeu, 2022. "Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    9. Racha El-Hajj & Rym Nesrine Guibadj & Aziz Moukrim & Mehdi Serairi, 2020. "A PSO based algorithm with an efficient optimal split procedure for the multiperiod vehicle routing problem with profit," Annals of Operations Research, Springer, vol. 291(1), pages 281-316, August.
    10. Dontas, Michael & Sideris, Georgios & Manousakis, Eleftherios G. & Zachariadis, Emmanouil E., 2023. "An adaptive memory matheuristic for the set orienteering problem," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1010-1023.
    11. Xia, Jun & Wang, Kai & Wang, Shuaian, 2019. "Drone scheduling to monitor vessels in emission control areas," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 174-196.
    12. Christos Orlis & Nicola Bianchessi & Roberto Roberti & Wout Dullaert, 2020. "The Team Orienteering Problem with Overlaps: An Application in Cash Logistics," Transportation Science, INFORMS, vol. 54(2), pages 470-487, March.
    13. Fang, Chao & Han, Zonglei & Wang, Wei & Zio, Enrico, 2023. "Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

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