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Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

Author

Listed:
  • Hao Qiang

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
    Jiangsu Province Engineering Research Center of High-Level Energy and Power Equipment, Changzhou University, Changzhou 213164, China)

  • Rui Ou

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

  • Yanchun Hu

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

  • Zhenyu Wu

    (Changzhou Xingyu Automotive Lighting Systems Co., Ltd., Changzhou 213022, China)

  • Xiaohua Zhang

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

Abstract

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Suggested Citation

  • Hao Qiang & Rui Ou & Yanchun Hu & Zhenyu Wu & Xiaohua Zhang, 2023. "Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16045-:d:1282167
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    References listed on IDEAS

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    1. Nolz, Pamela C. & Absi, Nabil & Feillet, Dominique & Seragiotto, Clóvis, 2022. "The consistent electric-Vehicle routing problem with backhauls and charging management," European Journal of Operational Research, Elsevier, vol. 302(2), pages 700-716.
    2. Seyfi, Majid & Alinaghian, Mahdi & Ghorbani, Erfan & Çatay, Bülent & Saeid Sabbagh, Mohammad, 2022. "Multi-mode hybrid electric vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    3. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    4. Nan Ding & Jingshuai Yang & Zhibin Han & Jianming Hao, 2022. "Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption," Mathematics, MDPI, vol. 10(17), pages 1-27, August.
    5. 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.
    6. Wenzhu Liao & Lin Liu & Jiazhuo Fu, 2019. "A Comparative Study on the Routing Problem of Electric and Fuel Vehicles Considering Carbon Trading," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    7. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Moon, Saedaseul & Lee, Deok-Joo, 2019. "An optimal electric vehicle investment model for consumers using total cost of ownership: A real option approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
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