IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8557-d861545.html
   My bibliography  Save this article

A Multi-Objective Optimization Model for the Intercity Railway Train Operation Plan: The Case of Beijing-Xiong’an ICR

Author

Listed:
  • Zilong Fan

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
    Shijiazhuang Railway Station, China Railway Beijing Group Co., Ltd., Shijiazhuang 050091, China)

  • Di Liu

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Wenyu Rong

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Chengrui Li

    (Tianjin Bullet Train Depot, China Railway Beijing Group Co., Ltd., Tianjin 300161, China)

Abstract

For intercity railway transportation enterprises, a reasonable intercity train operation plan is not only the foundation of the intercity railway operation organization, but also the key to the sustainable development of the intercity railway (ICR). In this paper, taking into account the economic benefits of railway transportation enterprises and the social benefits of passenger travel, an optimization model is established with the intercity railway train operation plan as the research object. The model aims to minimize the operating cost of railway transportation enterprises and minimize the travel time of passengers, and considers constraints such as passenger seat utilization, passenger flow, train frequency, and stops. It is a multi-objective optimization model that accumulates two objectives by introducing the passenger time value coefficient. According to the characteristics of the model, a genetic algorithm is designed to solve the model. Taking the Beijing-Xiong’an Intercity Railway (BXICR) as an example, the “smart business card” of China’s high-speed railway, two scenarios of passenger time value are designed, and the optimized train operation plan is obtained according to the existing OD passenger flow data, which verifies the effectiveness of the model and algorithm. The results show that compared with the original train operation plan, the number of stops per train of the optimized train operation plan under the two passenger time value scenarios decreased by 8.8% and 14.9%, the operating cost of the enterprise decreased by 7.7% and 1.6%, the travel time of passengers decreased by 0.7% and 1.5%, respectively. Under the condition of meeting the demand of passenger flow, the optimized train operation plan can effectively reduce the operating cost of enterprises and save the travel time of passengers, which is conducive to the sustainable development of intercity railways.

Suggested Citation

  • Zilong Fan & Di Liu & Wenyu Rong & Chengrui Li, 2022. "A Multi-Objective Optimization Model for the Intercity Railway Train Operation Plan: The Case of Beijing-Xiong’an ICR," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8557-:d:861545
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8557/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/14/8557/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Milan Dedík & Vladislav Zitrický & Michal Valla & Jozef Gašparík & Tomasz Figlus, 2022. "Optimization of Timetables on the Bratislava–Žilina–Košice Route in the Period after the End of the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Tseng, Yin-Yen & Verhoef, Erik T., 2008. "Value of time by time of day: A stated-preference study," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 607-618, August.
    3. Chang, Yu-Hern & Yeh, Chung-Hsing & Shen, Ching-Cheng, 2000. "A multiobjective model for passenger train services planning: application to Taiwan's high-speed rail line," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 91-106, February.
    4. Fezzi, Carlo & Bateman, Ian J. & Ferrini, Silvia, 2014. "Using revealed preferences to estimate the Value of Travel Time to recreation sites," Journal of Environmental Economics and Management, Elsevier, vol. 67(1), pages 58-70.
    5. Lin, Dung-Ying & Ku, Yu-Hsiung, 2014. "An implicit enumeration algorithm for the passenger service planning problem: Application to the Taiwan Railways Administration line," European Journal of Operational Research, Elsevier, vol. 238(3), pages 863-875.
    6. Qi, Jianguo & Yang, Lixing & Di, Zhen & Li, Shukai & Yang, Kai & Gao, Yuan, 2018. "Integrated optimization for train operation zone and stop plan with passenger distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 151-173.
    7. Huiling Fu & Lei Nie & Benjamin R. Sperry & Zhenhuan He, 2012. "Train Stop Scheduling in a High-Speed Rail Network by Utilizing a Two-Stage Approach," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-11, November.
    8. Dingjun Chen & Shaoquan Ni & Chang’an Xu & Hongxia Lv & Simin Wang, 2016. "High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, January.
    9. Jianguo Qi & Shukai Li & Yuan Gao & Kai Yang & Pei Liu, 2018. "Joint optimization model for train scheduling and train stop planning with passengers distribution on railway corridors," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(4), pages 556-570, April.
    10. Robenek, Tomáš & Azadeh, Shadi Sharif & Maknoon, Yousef & de Lapparent, Matthieu & Bierlaire, Michel, 2018. "Train timetable design under elastic passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 19-38.
    11. Xin Zhang & Lei Nie & Xin Wu & Yu Ke, 2020. "How to Optimize Train Stops under Diverse Passenger Demand: a New Line Planning Method for Large-Scale High-Speed Rail Networks," Networks and Spatial Economics, Springer, vol. 20(4), pages 963-988, December.
    12. Yang, Lixing & Qi, Jianguo & Li, Shukai & Gao, Yuan, 2016. "Collaborative optimization for train scheduling and train stop planning on high-speed railways," Omega, Elsevier, vol. 64(C), pages 57-76.
    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. Xin Zhang & Lei Nie & Xin Wu & Yu Ke, 2020. "How to Optimize Train Stops under Diverse Passenger Demand: a New Line Planning Method for Large-Scale High-Speed Rail Networks," Networks and Spatial Economics, Springer, vol. 20(4), pages 963-988, December.
    2. Pu, Song & Zhan, Shuguang, 2021. "Two-stage robust railway line-planning approach with passenger demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Xu, Guangming & Liu, Yihan & Gao, Yihan & Liu, Wei, 2023. "Integrated optimization of train stopping plan and seat allocation scheme for railway systems under equilibrium travel choice and elastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    4. Jin Qin & Xiqiong Li & Kang Yang & Guangming Xu, 2022. "Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study," Mathematics, MDPI, vol. 10(10), pages 1-17, May.
    5. Tian, Xiaopeng & Niu, Huimin, 2020. "Optimization of demand-oriented train timetables under overtaking operations: A surrogate-dual-variable column generation for eliminating indivisibility," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 143-173.
    6. Ali Shahabi & Sadigh Raissi & Kaveh Khalili-Damghani & Meysam Rafei, 2021. "Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization methodology," Operational Research, Springer, vol. 21(3), pages 1691-1721, September.
    7. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    8. Svetla Stoilova, 2020. "An Integrated Multi-Criteria and Multi-Objective Optimization Approach for Establishing the Transport Plan of Intercity Trains," Sustainability, MDPI, vol. 12(2), pages 1-24, January.
    9. Hongguo Ren & Zhenbao Wang & Yanyan Chen, 2020. "Optimal Express Bus Routes Design with Limited-Stop Services for Long-Distance Commuters," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    10. Liang, Jinpeng & Zang, Guangzhi & Liu, Haitao & Zheng, Jianfeng & Gao, Ziyou, 2023. "Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy," Omega, Elsevier, vol. 117(C).
    11. Shuo Zhao & Xiwei Mi & Zhenyi Li, 2019. "A Stop-Probability Approach for O-D Service Frequency on High-Speed Railway Lines," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
    12. Jianqiang Wang & Wenlong Zhao & Chenglin Liu & Zhipeng Huang, 2023. "A System Optimization Approach for Trains’ Operation Plan with a Time Flexible Pricing Strategy for High-Speed Rail Corridors," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    13. Qi, Jianguo & Yang, Lixing & Di, Zhen & Li, Shukai & Yang, Kai & Gao, Yuan, 2018. "Integrated optimization for train operation zone and stop plan with passenger distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 151-173.
    14. Jiang, Feng & Cacchiani, Valentina & Toth, Paolo, 2017. "Train timetabling by skip-stop planning in highly congested lines," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 149-174.
    15. Cacchiani, Valentina & Qi, Jianguo & Yang, Lixing, 2020. "Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 1-29.
    16. Tatsuki Yamauchi & Mizuyo Takamatsu & Shinji Imahori, 2023. "Optimizing train stopping patterns for congestion management," Public Transport, Springer, vol. 15(1), pages 1-29, March.
    17. Chew, Joanne S.C. & Zhang, Lele & Gan, Heng S., 2019. "Optimizing limited-stop services with vehicle assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 228-246.
    18. Pan, Hanchuan & Liu, Zhigang & Yang, Lixing & Liang, Zhe & Wu, Qiang & Li, Sijie, 2021. "A column generation-based approach for integrated vehicle and crew scheduling on a single metro line with the fully automatic operation system by partial supervision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    19. Li, Zhujun & Shalaby, Amer & Roorda, Matthew J. & Mao, Baohua, 2021. "Urban rail service design for collaborative passenger and freight transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    20. Zhang, Yongxiang & Peng, Qiyuan & Lu, Gongyuan & Zhong, Qingwei & Yan, Xu & Zhou, Xuesong, 2022. "Integrated line planning and train timetabling through price-based cross-resolution feedback mechanism," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 240-277.

    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:14:y:2022:i:14:p:8557-:d:861545. 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.