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A Particle Swarm Optimisation for Vehicle Routing Problem with Time Windows

Author

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  • The Jin Ai
  • Voratas Kachitvichyanukul

Abstract

A heuristic based on Particle Swarm Optimisation (PSO) algorithm for solving VRPTW, which is an extension of PSO application for the Capacitated Vehicle Routing Problem (CVRP) (Ai and Kachitvichyanukul, 2007), is presented in this paper. A computational experiment is carried out by running the proposed algorithm with the VRPTW benchmark data set of Solomon (1987). The results show that the proposed algorithm is able to provide VRPTW solutions that are very close to its optimal solutions for problems with 25 and 50 customers within reasonably short of computational time.

Suggested Citation

  • The Jin Ai & Voratas Kachitvichyanukul, 2009. "A Particle Swarm Optimisation for Vehicle Routing Problem with Time Windows," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 6(4), pages 519-537.
    • Handle: RePEc:ids:ijores:v:6:y:2009:i:4:p:519-537
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    Citations

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    Cited by:

    1. Mohsen Emadikhiav & David Bergman & Robert Day, 2020. "Consistent Routing and Scheduling with Simultaneous Pickups and Deliveries," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1937-1955, August.
    2. Zühal Kartal & Mohan Krishnamoorthy & Andreas T. Ernst, 2019. "Heuristic algorithms for the single allocation p-hub center problem with routing considerations," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 99-145, March.
    3. Shih-Che Lo, 2022. "A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management," Logistics, MDPI, vol. 6(3), pages 1-15, September.
    4. Themistoklis Stamadianos & Andromachi Taxidou & Magdalene Marinaki & Yannis Marinakis, 2024. "Swarm intelligence and nature inspired algorithms for solving vehicle routing problems: a survey," Operational Research, Springer, vol. 24(3), pages 1-45, September.
    5. Singh, Bikramjit & Singh, Amarinder, 2023. "Hybrid particle swarm optimization for pure integer linear solid transportation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 243-266.
    6. Li, Chongshou & Gong, Lijun & Luo, Zhixing & Lim, Andrew, 2019. "A branch-and-price-and-cut algorithm for a pickup and delivery problem in retailing," Omega, Elsevier, vol. 89(C), pages 71-91.
    7. Lu Gan & Li Wang & Lin Hu, 2017. "Gathered Village Location Optimization for Chinese Sustainable Urbanization Using an Integrated MODM Approach under Bi-Uncertain Environment," Sustainability, MDPI, vol. 9(10), pages 1-25, October.
    8. Aziez, Imadeddine & Côté, Jean-François & Coelho, Leandro C., 2022. "Fleet sizing and routing of healthcare automated guided vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    9. Aziez, Imadeddine & Côté, Jean-François & Coelho, Leandro C., 2020. "Exact algorithms for the multi-pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 284(3), pages 906-919.
    10. Min-Xia Zhang & Hong-Fan Yan & Jia-Yu Wu & Yu-Jun Zheng, 2020. "Quarantine Vehicle Scheduling for Transferring High-Risk Individuals in Epidemic Areas," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    11. Naccache, Salma & Côté, Jean-François & Coelho, Leandro C., 2018. "The multi-pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 269(1), pages 353-362.
    12. Samuel Reong & Hui-Ming Wee & Yu-Lin Hsiao, 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    13. Rau, Hsin & Budiman, Syarif Daniel & Widyadana, Gede Agus, 2018. "Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 51-75.
    14. M. Alinaghian & M. Ghazanfari & N. Norouzi & H. Nouralizadeh, 2017. "A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization," Networks and Spatial Economics, Springer, vol. 17(4), pages 1185-1211, December.

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