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Token-specific deep reinforcement learning for energy-efficient capacitated electric vehicle routing problems

Author

Listed:
  • Guan, Qingshu
  • Cao, Hui
  • Tan, Junkai
  • Jia, Lixin
  • Yan, Dapeng
  • Chen, Badong

Abstract

Nowadays, the electric vehicle routing problem (EVRP) has garnered considerable attention in sustainable energy and transportation research, driven by the environmental promise of electric vehicles (EVs) to reduce carbon emissions and lower energy consumption. However, existing approaches to EVRPs predominantly aim to minimize travel distance, neglecting real-world factors such as road conditions and driving dynamics, which substantially influence the final energy consumption.

Suggested Citation

  • Guan, Qingshu & Cao, Hui & Tan, Junkai & Jia, Lixin & Yan, Dapeng & Chen, Badong, 2025. "Token-specific deep reinforcement learning for energy-efficient capacitated electric vehicle routing problems," Applied Energy, Elsevier, vol. 396(C).
    • Handle: RePEc:eee:appene:v:396:y:2025:i:c:s030626192501044x
    DOI: 10.1016/j.apenergy.2025.126314
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    References listed on IDEAS

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    1. Wang, Yi & Qiu, Dawei & He, Yinglong & Zhou, Quan & Strbac, Goran, 2023. "Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling," Energy, Elsevier, vol. 284(C).
    2. Sistig, Hubert Maximilian & Sauer, Dirk Uwe, 2023. "Metaheuristic for the integrated electric vehicle and crew scheduling problem," Applied Energy, Elsevier, vol. 339(C).
    3. Erdoğan, Sevgi & Miller-Hooks, Elise, 2012. "A Green Vehicle Routing Problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 100-114.
    4. Tang, Mengcheng & Zhuang, Weichao & Li, Bingbing & Liu, Haoji & Song, Ziyou & Yin, Guodong, 2023. "Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer," Applied Energy, Elsevier, vol. 350(C).
    5. Ayyad, Mahmoud & Yang, Lisheng & Ahmed, Alaa & Shalaby, Ahmed & Huang, Jianuo & Mi, Jia & Datla, Raju & Zuo, Lei & Hajj, Muhammad R., 2025. "System identification of oscillating surge wave energy converter using physics-informed neural network," Applied Energy, Elsevier, vol. 378(PA).
    6. Wang, Zihao & Yu, Donghan & Wu, Zhe, 2025. "Real-time machine-learning-based optimization using Input Convex Long Short-Term Memory network," Applied Energy, Elsevier, vol. 377(PB).
    7. 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.
    8. Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    9. Ece Naz Duman & Duygu Taş & Bülent Çatay, 2022. "Branch-and-price-and-cut methods for the electric vehicle routing problem with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5332-5353, September.
    10. Zhang, Zhe & Yu, Qing & Gao, Kun & He, Hong-Di & Liu, Yang & Huang, Haichao, 2025. "Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure," Applied Energy, Elsevier, vol. 377(PA).
    11. Wang, Jiawei & Guo, Qinglai & Sun, Hongbin, 2024. "Planning approach for integrating charging stations and renewable energy sources in low-carbon logistics delivery," Applied Energy, Elsevier, vol. 372(C).
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