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Hybrid electric vehicle routing problem with mode selection

Author

Listed:
  • Lu Zhen
  • Ziheng Xu
  • Chengle Ma
  • Liyang Xiao

Abstract

With the development of green logistics, logistics companies gradually are paying attention to the application of hybrid electric vehicles (HEVs). HEVs have the advantages of low energy consumption and pollution, while their disadvantage mainly lies in their limited continuous driving range. Therefore, it is necessary to optimize the use of fuel during the distribution process. We study the mode selection system in HEVs based on the background of green logistics and the above characteristics of HEVs. The mode selection system can adjust the driving mode of the HEV according to different road conditions to obtain the optimal use of fuel. In this paper, we propose a new study of a hybrid electric vehicle routing problem with mode selection. This problem is formulated as a mixed integer linear programming model. An improved particle swarm optimization algorithm (IPSO) is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. The experimental results show that our proposed algorithm not only obtains the optimal solution for some small-scale problem instances and some medium-scale problems but also solves some large-scale situations (one hundred customers, eleven vehicles, eleven charging stations, eleven gas stations and four modes) within an hour.

Suggested Citation

  • Lu Zhen & Ziheng Xu & Chengle Ma & Liyang Xiao, 2020. "Hybrid electric vehicle routing problem with mode selection," International Journal of Production Research, Taylor & Francis Journals, vol. 58(2), pages 562-576, January.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:2:p:562-576
    DOI: 10.1080/00207543.2019.1598593
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    Cited by:

    1. Baek, Jieun & Choi, Yosoon, 2023. "Optimal installation and operation planning of parking spaces for solar-powered electric vehicles using hemispherical images," Renewable Energy, Elsevier, vol. 219(P1).
    2. 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.
    3. 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).
    4. Wang, Ruiting & Keyantuo, Patrick & Zeng, Teng & Sandoval, Jairo & Vishwanath, Aashrith & Borhan, Hoseinali & Moura, Scott, 2024. "Robust routing for a mixed fleet of heavy-duty trucks with pickup and delivery under energy consumption uncertainty," Applied Energy, Elsevier, vol. 368(C).
    5. Jun-bin Wang & Lufei Huang, 2021. "A Game-Theoretic Analytical Approach for Fostering Energy-Saving Innovation in the Electric Vehicle Supply Chain," SAGE Open, , vol. 11(2), pages 21582440211, June.
    6. Liang Sun, 2022. "Modeling and evolutionary algorithm for solving a multi-depot mixed vehicle routing problem with uncertain travel times," Journal of Heuristics, Springer, vol. 28(5), pages 619-651, December.
    7. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    8. 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.
    9. Lin, Na & Akkerman, Renzo & Kanellopoulos, Argyris & Hu, Xiangpei & Wang, Xuping & Ruan, Junhu, 2023. "Vehicle routing with heterogeneous service types: Optimizing post-harvest preprocessing operations for fruits and vegetables in short food supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    10. Pablo A. Miranda-Gonzalez & Javier Maturana-Ross & Carola A. Blazquez & Guillermo Cabrera-Guerrero, 2021. "Exact Formulation and Analysis for the Bi-Objective Insular Traveling Salesman Problem," Mathematics, MDPI, vol. 9(21), pages 1-33, October.
    11. Ferreira, Kamyla Maria & de Queiroz, Thiago Alves & Munari, Pedro & Toledo, Franklina Maria Bragion, 2024. "A variable neighborhood search for the green vehicle routing problem with two-dimensional loading constraints and split delivery," European Journal of Operational Research, Elsevier, vol. 316(2), pages 597-616.
    12. 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).
    13. Yanjun Shi & Na Lin & Qiaomei Han & Tongliang Zhang & Weiming Shen, 2020. "A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    14. 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).
    15. Wen Yi & Ying Terk Lim & Huiwen Wang & Lu Zhen & Xin Zhou, 2024. "Construction Waste Transportation Planning under Uncertainty: Mathematical Models and Numerical Experiments," Mathematics, MDPI, vol. 12(19), pages 1-17, September.
    16. Granillo-Macías, Rafael, 2021. "Logistics optimization through a social approach for food distribution," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).

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