IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2700-d1615166.html
   My bibliography  Save this article

Collaboration and Resource Sharing for the Multi-Depot Electric Vehicle Routing Problem with Time Windows and Dynamic Customer Demands

Author

Listed:
  • Yong Wang

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

  • Can Chen

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

  • Yuanhan Wei

    (School of Economics and Management, Dalian University of Technology, Dalian 116024, China)

  • Yuanfan Wei

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

  • Haizhong Wang

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

Abstract

With increasingly diverse customer demands and the rapid growth of the new energy logistics industry, establishing a sustainable and responsive logistics network is critical. In a multi-depot logistics network, adopting collaborative distribution and resource sharing can significantly improve operational efficiency. This study proposes collaboration and resource sharing for a multi-depot electric vehicle (EV) routing problem with time windows and dynamic customer demands. A bi-objective optimization model is formulated to minimize the total operating costs and the number of EVs. To solve the model, a novel hybrid algorithm combining a mini-batch k -means clustering algorithm with an improved multi-objective differential evolutionary algorithm (IMODE) is proposed. This algorithm integrates genetic operations and a non-dominated sorting operation to enhance the solution quality. The strategies for dynamically inserting customer demands and charging stations are embedded within the algorithm to identify Pareto-optimal solutions effectively. The algorithm’s efficacy and applicability are verified through comparisons with the multi-objective genetic algorithm, the multi-objective evolutionary algorithm, the multi-objective particle swarm optimization algorithm, multi-objective ant colony optimization, and a multi-objective tabu search. Additionally, a case study of a new energy logistics company in Chongqing City, China demonstrates that the proposed method significantly reduces the logistics operating costs and improves logistics network efficiency. Sensitivity analysis considering different dynamic customer demand response modes and distribution strategies provides insights for reducing the total operating costs and enhancing distribution efficiency. The findings offer essential insights for promoting an environmentally sustainable and resource-efficient city.

Suggested Citation

  • Yong Wang & Can Chen & Yuanhan Wei & Yuanfan Wei & Haizhong Wang, 2025. "Collaboration and Resource Sharing for the Multi-Depot Electric Vehicle Routing Problem with Time Windows and Dynamic Customer Demands," Sustainability, MDPI, vol. 17(6), pages 1-38, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2700-:d:1615166
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2700/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/6/2700/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Yuanhan & Wang, Yong & Hu, Xiangpei, 2025. "The two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    2. Gkiotsalitis, K. & Iliopoulou, C. & Kepaptsoglou, K., 2023. "An exact approach for the multi-depot electric bus scheduling problem with time windows," European Journal of Operational Research, Elsevier, vol. 306(1), pages 189-206.
    3. 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).
    4. 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.
    5. Yong Wang & Yingying Yuan & Xiangyang Guan & Haizhong Wang & Yong Liu & Maozeng Xu, 2019. "Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows," Sustainability, MDPI, vol. 11(12), pages 1-30, June.
    6. Lahcene Guezouli & Samir Abdelhamid, 2018. "Multi-objective optimisation using genetic algorithm based clustering for multi-depot heterogeneous fleet vehicle routing problem with time windows," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 13(3), pages 332-349.
    7. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    8. Sehrish Malik & DoHyeun Kim, 2019. "Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data," Sustainability, MDPI, vol. 11(12), pages 1-26, June.
    9. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    10. Lera-Romero, Gonzalo & Miranda Bront, Juan José & Soulignac, Francisco J., 2024. "A branch-cut-and-price algorithm for the time-dependent electric vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 312(3), pages 978-995.
    11. Zhang, Shuai & Gajpal, Yuvraj & Appadoo, S.S. & Abdulkader, M.M.S., 2018. "Electric vehicle routing problem with recharging stations for minimizing energy consumption," International Journal of Production Economics, Elsevier, vol. 203(C), pages 404-413.
    12. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Azra Ghobadi & Mohammad Fallah & Reza Tavakkoli-Moghaddam & Hamed Kazemipoor, 2022. "A Fuzzy Two-Echelon Model to Optimize Energy Consumption in an Urban Logistics Network with Electric Vehicles," Sustainability, MDPI, vol. 14(21), pages 1-31, October.
    2. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    4. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    5. Erfan Ghorbani & Mahdi Alinaghian & Gevork. B. Gharehpetian & Sajad Mohammadi & Guido Perboli, 2020. "A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification," Sustainability, MDPI, vol. 12(21), pages 1-71, October.
    6. Qiuping Ni & Yuanxiang Tang, 2023. "A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    7. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    8. Virginia Casella & Daniel Fernandez Valderrama & Giulio Ferro & Riccardo Minciardi & Massimo Paolucci & Luca Parodi & Michela Robba, 2022. "Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management," Energies, MDPI, vol. 15(11), pages 1-23, May.
    9. Wei Xu & Chenghao Zhang & Ming Cheng & Yucheng Huang, 2022. "Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery: Mathematical Modeling and Adaptive Large Neighborhood Search Heuristic Method," Energies, MDPI, vol. 15(23), pages 1-25, December.
    10. Ana Bricia Galindo-Muro & Riccardo Cespi & Stephany Isabel Vallarta-Serrano, 2023. "Applications of Electric Vehicles in Instant Deliveries," Energies, MDPI, vol. 16(4), pages 1-18, February.
    11. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    12. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    13. Lai, Kexing & Chen, Tao & Natarajan, Balasubramaniam, 2020. "Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation," Energy, Elsevier, vol. 204(C).
    14. Wu, Guoyuan & Peng, Dongbo & Boriboonsomsin, Kanok, 2024. "Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification," Institute of Transportation Studies, Working Paper Series qt2qz0n2gv, Institute of Transportation Studies, UC Davis.
    15. Alexandre M. Florio & Nabil Absi & Dominique Feillet, 2021. "Routing Electric Vehicles on Congested Street Networks," Transportation Science, INFORMS, vol. 55(1), pages 238-256, 1-2.
    16. Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.
    17. Wang, Mengtong & Miao, Lixin & Zhang, Canrong, 2021. "A branch-and-price algorithm for a green location routing problem with multi-type charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    18. Avenali, Alessandro & De Santis, Daniele & Giagnorio, Mirko & Matteucci, Giorgio, 2024. "Bus fleet decarbonization under macroeconomic and technological uncertainties: A real options approach to support decision-making," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
    19. 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.
    20. Xabier A. Martin & Marc Escoto & Antoni Guerrero & Angel A. Juan, 2024. "Battery Management in Electric Vehicle Routing Problems: A Review," Energies, MDPI, vol. 17(5), pages 1-25, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2700-:d:1615166. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.