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Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows

Author

Listed:
  • Yong Wang

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Jiayi Zhe

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Xiuwen Wang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Yaoyao Sun

    (School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Haizhong Wang

    (School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97330, USA)

Abstract

Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k -medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k -medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.

Suggested Citation

  • Yong Wang & Jiayi Zhe & Xiuwen Wang & Yaoyao Sun & Haizhong Wang, 2022. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows," Sustainability, MDPI, vol. 14(11), pages 1-37, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6709-:d:828411
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    References listed on IDEAS

    as
    1. Jasmin Grabenschweiger & Karl F. Doerner & Richard F. Hartl & Martin W. P. Savelsbergh, 2021. "The vehicle routing problem with heterogeneous locker boxes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 113-142, March.
    2. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Los, Johan & Schulte, Frederik & Spaan, Matthijs T.J. & Negenborn, Rudy R., 2020. "The value of information sharing for platform-based collaborative vehicle routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    4. Fernández, Elena & Roca-Riu, Mireia & Speranza, M. Grazia, 2018. "The Shared Customer Collaboration Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1078-1093.
    5. Yang, Fei & Dai, Ying & Ma, Zu-Jun, 2020. "A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    6. Haitao Xu & Pan Pu & Feng Duan, 2018. "A Hybrid Ant Colony Optimization for Dynamic Multidepot Vehicle Routing Problem," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-10, September.
    7. Eskandarpour, Majid & Ouelhadj, Djamila & Hatami, Sara & Juan, Angel A. & Khosravi, Banafsheh, 2019. "Enhanced multi-directional local search for the bi-objective heterogeneous vehicle routing problem with multiple driving ranges," European Journal of Operational Research, Elsevier, vol. 277(2), pages 479-491.
    8. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    9. Yong Wang & Qin Li & Xiangyang Guan & Jianxin Fan & Yong Liu & Haizhong Wang, 2020. "Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries," Sustainability, MDPI, vol. 12(15), pages 1-33, July.
    10. Asadi, Ehsan & Habibi, Farhad & Nickel, Stefan & Sahebi, Hadi, 2018. "A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain," Applied Energy, Elsevier, vol. 228(C), pages 2235-2261.
    11. Zhen, Lu & Ma, Chengle & Wang, Kai & Xiao, Liyang & Zhang, Wei, 2020. "Multi-depot multi-trip vehicle routing problem with time windows and release dates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    12. Zhang, Junlong & Lam, William H.K. & Chen, Bi Yu, 2016. "On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows," European Journal of Operational Research, Elsevier, vol. 249(1), pages 144-154.
    13. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    14. Mancini, Simona & Gansterer, Margaretha & Hartl, Richard F., 2021. "The collaborative consistent vehicle routing problem with workload balance," European Journal of Operational Research, Elsevier, vol. 293(3), pages 955-965.
    15. Zhang, Qihuan & Wang, Ziteng & Huang, Min & Yu, Yang & Fang, Shu-Cherng, 2022. "Heterogeneous multi-depot collaborative vehicle routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 160(C), pages 1-20.
    16. Haitao Xu & Pan Pu & Feng Duan, 2018. "Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-13, February.
    17. Lin, Qiuzhen & Li, Jianqiang & Du, Zhihua & Chen, Jianyong & Ming, Zhong, 2015. "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, Elsevier, vol. 247(3), pages 732-744.
    18. Dayarian, Iman & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2016. "An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 95-123.
    19. Russell W. Bent & Pascal Van Hentenryck, 2004. "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers," Operations Research, INFORMS, vol. 52(6), pages 977-987, December.
    20. Sihan Wang & Cheng Han & Yang Yu & Min Huang & Wei Sun & Ikou Kaku, 2022. "Reducing Carbon Emissions for the Vehicle Routing Problem by Utilizing Multiple Depots," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    21. F. Errico & G. Desaulniers & M. Gendreau & W. Rei & L.-M. Rousseau, 2018. "The vehicle routing problem with hard time windows and stochastic service times," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 223-251, September.
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