IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n3087279.html

Optimal Charging Scheduling and Management with Bus‐Driver‐Trip Assignment considering Mealtime Windows for an Electric Bus Line

Author

Listed:
  • Yang Jiang
  • Tong He

Abstract

Compared to a charging scheduling and management problem characterized by predetermined trip assignment, this study takes bus and driver scheduling into account, and mealtime windows must be guaranteed as one of the major labor regulations. A discretized mixed‐integer linear programming (MIPL) model is developed based on a single electric bus route. We aim to obtain fast and high‐quality global solutions for this problem, and the model can be easily executed by bus operators by directly invoking an available optimization solver such as IBM ILOG CPLEX. We test our model on a real round‐trip bus route. Numerical experiments show that CPLEX takes approximately 6 sec to obtain an optimal solution. The model can not only reasonably arrange daily trips for each electric bus and driver but also effectively determine the optimal charging schedule and management for an electric bus line. Besides, we analyze the sensitivity of the key parameters in the model. With the increase in the drivers’ maximum workload, the drivers’ average idle time decreases by approximately 11.25%. The objective value decreases by approximately 38.71% and 40.04% with increases in the battery capacity and fleet size, respectively, and the objective value increases by approximately 30.06% with the decrease in the initial battery driving range. In addition, we compare the effectiveness of our time discretization modeling method in solving the same case study to that from other similar studies, and the validity of our method can be verified by the calculation time. We also compare the computational efficiency of CPLEX in solving the same case study problem with and without implementing valid inequalities, and the computational efficiency of the valid inequality method is greatly improved. Finally, through the testing of a multiline network, the potential application of the model to a large‐scale traffic network is verified.

Suggested Citation

  • Yang Jiang & Tong He, 2022. "Optimal Charging Scheduling and Management with Bus‐Driver‐Trip Assignment considering Mealtime Windows for an Electric Bus Line," Complexity, John Wiley & Sons, vol. 2022(1).
  • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:3087279
    DOI: 10.1155/2022/3087279
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/3087279
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/3087279?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Langbroek, Joram H.M. & Cebecauer, Matej & Malmsten, Jon & Franklin, Joel P. & Susilo, Yusak O. & Georén, Peter, 2019. "Electric vehicle rental and electric vehicle adoption," Research in Transportation Economics, Elsevier, vol. 73(C), pages 72-82.
    2. F. Zeynep Sargut & Caner Altuntaş & Dilek Cetin Tulazoğlu, 2017. "Multi-objective integrated acyclic crew rostering and vehicle assignment problem in public bus transportation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1071-1096, October.
    3. Lajunen, Antti & Lipman, Timothy, 2016. "Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses," Energy, Elsevier, vol. 106(C), pages 329-342.
    4. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    5. Rajesh Paleti & Peter Vovsha & Danny Givon & Yehoshua Birotker, 2015. "Impact of individual daily travel pattern on value of time," Transportation, Springer, vol. 42(6), pages 1003-1017, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yaoyao & Jin, Jian Gang & Ibarra-Rojas, Omar J. & Xu, Min & Cao, Zhichao, 2026. "A branch-and-price algorithm for integrated optimization on vehicle and crew scheduling of electric bus system," Transportation Research Part B: Methodological, Elsevier, vol. 206(C).

    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. Wang, Yusheng & Huang, Yongxi & Xu, Jiuping & Barclay, Nicole, 2017. "Optimal recharging scheduling for urban electric buses: A case study in Davis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 115-132.
    2. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    3. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    4. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    5. McCluskey, Jac & Druitt, Tom & Larkin, Charles, 2025. "Sustainability in transit: Assessing the economic case for electric bus adoption in the UK," Transport Policy, Elsevier, vol. 162(C), pages 493-508.
    6. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    7. Sofia Dahlgren & Jonas Ammenberg, 2021. "Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus Technologies," Sustainability, MDPI, vol. 13(3), pages 1-30, January.
    8. Paulo J. G. Ribeiro & José F. G. Mendes, 2022. "Towards Zero CO 2 Emissions from Public Transport: The Pathway to the Decarbonization of the Portuguese Urban Bus Fleet," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    9. Yue Liu & Jun Chen & Weiguang Wu & Jiao Ye, 2019. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    10. Yang, Chengcheng & Jin, Sheng & Gao, Kun & Rong, Donglei & Bai, Congcong & Gao, Xi & Shen, Xinyi & Yao, Wenbin & Jérémie, Adjé Alagbé, 2025. "Collaborative optimization of battery electric bus scheduling and charging considering arterial signal coordination under connected environment," Applied Energy, Elsevier, vol. 394(C).
    11. Ana Paias & Marta Mesquita & Margarida Moz & Margarida Pato, 2021. "A network flow-based algorithm for bus driver rerostering," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(2), pages 543-576, June.
    12. Salari, Nasir, 2022. "Electric vehicles adoption behaviour: Synthesising the technology readiness index with environmentalism values and instrumental attributes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 60-81.
    13. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    14. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    15. Chen, Yifan & Yang, Liuquan & Yang, Chao & Wang, Weida & Zha, Mingjun & Gao, Pu & Liu, Hui, 2024. "Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition," Energy, Elsevier, vol. 307(C).
    16. Anton, Sorin Gabriel, 2021. "The impact of temperature increase on firm profitability. Empirical evidence from the European energy and gas sectors," Applied Energy, Elsevier, vol. 295(C).
    17. Li, Xiangyi & Castellanos, Sebastian & Maassen, Anne, 2018. "Emerging trends and innovations for electric bus adoption—a comparative case study of contracting and financing of 22 cities in the Americas, Asia-Pacific, and Europe," Research in Transportation Economics, Elsevier, vol. 69(C), pages 470-481.
    18. Daryabari, Mohamad K. & Keypour, Reza & Golmohamadi, Hessam, 2020. "Stochastic energy management of responsive plug-in electric vehicles characterizing parking lot aggregators," Applied Energy, Elsevier, vol. 279(C).
    19. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
    20. Chen, Ruihu & Yang, Chao & Ma, Yue & Wang, Weida & Wang, Muyao & Du, Xuelong, 2022. "Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set," Applied Energy, Elsevier, vol. 323(C).

    More about this item

    Statistics

    Access and download statistics

    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:wly:complx:v:2022:y:2022:i:1:n:3087279. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.