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Consistent vehicle routing problem with simultaneous distribution and collection

Author

Listed:
  • Lu Zhen
  • Wenya Lv
  • Kai Wang
  • Chengle Ma
  • Ziheng Xu

Abstract

To improve customer service in the reverse logistics, this article defines a new variant of the vehicle routing problem (VRP) by combining the consistent VRP (ConVRP) and the VRP with simultaneous distribution and collection (VRPSDC). This new variant is called the consistent vehicle routing problem with simultaneous distribution and collection, for which a mixed-integer programming model is formulated. To solve this problem, three heuristics are proposed on the basis of the record-to-record (RTR) travel algorithm, the local search with variable neighbourhood search (LSVNS), and the tabu search-based method. Numerical experiments are performed to validate the efficiency of our proposed solution methods and the effectiveness of the proposed model. The results show that the RTR-based heuristic has an advantage in small-scale instances. However, for medium-scale instances, the best option is the LSVNS-based heuristic, which can solve instances with 40 customers and 5 days within 10 s. Moreover, the LSVNS-based heuristic can solve large-scale instances with 200 customers and 5 days 3 hours.

Suggested Citation

  • Lu Zhen & Wenya Lv & Kai Wang & Chengle Ma & Ziheng Xu, 2020. "Consistent vehicle routing problem with simultaneous distribution and collection," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 813-830, May.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:5:p:813-830
    DOI: 10.1080/01605682.2019.1590134
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    Cited by:

    1. Zhen, Lu & Gao, Jiajing & Tan, Zheyi & Laporte, Gilbert & Baldacci, Roberto, 2023. "Territorial design for customers with demand frequency," European Journal of Operational Research, Elsevier, vol. 309(1), pages 82-101.
    2. Mehrnaz Bathaee & Hamed Nozari & Agnieszka Szmelter-Jarosz, 2023. "Designing a New Location-Allocation and Routing Model with Simultaneous Pick-Up and Delivery in a Closed-Loop Supply Chain Network under Uncertainty," Logistics, MDPI, vol. 7(1), pages 1-33, January.
    3. Md Saiful Islam & Md Sarowar Morshed & Md. Noor-E-Alam, 2022. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3023-3041, November.
    4. Cui, Weiwei & Yang, Yiran & Di, Lei, 2023. "Modeling and optimization for static-dynamic routing of a vehicle with additive manufacturing equipment," International Journal of Production Economics, Elsevier, vol. 257(C).
    5. Cherkesly, Marilène & Gschwind, Timo, 2022. "The pickup and delivery problem with time windows, multiple stacks, and handling operations," European Journal of Operational Research, Elsevier, vol. 301(2), pages 647-666.
    6. Mohammed Alkahtani & Aiman Ziout & Bashir Salah & Moath Alatefi & Abd Elatty E. Abd Elgawad & Ahmed Badwelan & Umar Syarif, 2021. "An Insight into Reverse Logistics with a Focus on Collection Systems," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    7. Lei Chen & Haiyan Ma & Yi Wang & Feng Li, 2022. "Vehicle Routing Problem for the Simultaneous Pickup and Delivery of Lithium Batteries of Small Power Vehicles under Charging and Swapping Mode," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    8. Zhou, Jian & Li, Hui & Gu, Yujie & Zhao, Mingxuan & Xie, Xuehui & Zheng, Haoran & Fang, Xinghua, 2021. "A novel two-phase approach for the bi-objective simultaneous delivery and pickup problem with fuzzy pickup demands," International Journal of Production Economics, Elsevier, vol. 234(C).

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