IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2375-d1608045.html
   My bibliography  Save this article

A Hybrid Optimization Approach Combining Rolling Horizon with Deep-Learning-Embedded NSGA-II Algorithm for High-Speed Railway Train Rescheduling Under Interruption Conditions

Author

Listed:
  • Wenqiang Zhao

    (The School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Leishan Zhou

    (The School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Chang Han

    (The School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals.

Suggested Citation

  • Wenqiang Zhao & Leishan Zhou & Chang Han, 2025. "A Hybrid Optimization Approach Combining Rolling Horizon with Deep-Learning-Embedded NSGA-II Algorithm for High-Speed Railway Train Rescheduling Under Interruption Conditions," Sustainability, MDPI, vol. 17(6), pages 1-30, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2375-:d:1608045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2375/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/6/2375/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Törnquist, Johanna, 2007. "Railway traffic disturbance management--An experimental analysis of disturbance complexity, management objectives and limitations in planning horizon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 249-266, March.
    2. Sahin, Ismail, 1999. "Railway traffic control and train scheduling based oninter-train conflict management," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 511-534, September.
    3. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    4. Mattia Pasqui & Lorenzo Becchi & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gianluigi Fioriti & Carlo Carcasci, 2024. "Community Battery for Collective Self-Consumption and Energy Arbitrage: Independence Growth vs. Investment Cost-Effectiveness," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    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. repec:iim:iimawp:12906 is not listed on IDEAS
    2. Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
    3. Burdett, R.L. & Kozan, E., 2010. "A disjunctive graph model and framework for constructing new train schedules," European Journal of Operational Research, Elsevier, vol. 200(1), pages 85-98, January.
    4. Yu-Jun Zheng, 2018. "Emergency Train Scheduling on Chinese High-Speed Railways," Transportation Science, INFORMS, vol. 52(5), pages 1077-1091, October.
    5. Mascis, Alessandro & Pacciarelli, Dario, 2002. "Job-shop scheduling with blocking and no-wait constraints," European Journal of Operational Research, Elsevier, vol. 143(3), pages 498-517, December.
    6. Ballis, Athanasios & Golias, John, 2002. "Comparative evaluation of existing and innovative rail-road freight transport terminals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(7), pages 593-611, August.
    7. Nielsen, Lars Kjær & Kroon, Leo & Maróti, Gábor, 2012. "A rolling horizon approach for disruption management of railway rolling stock," European Journal of Operational Research, Elsevier, vol. 220(2), pages 496-509.
    8. Huang, Wencheng & Zhang, Yue & Yu, Yaocheng & Xu, Yifei & Xu, Minhao & Zhang, Rui & De Dieu, Gatesi Jean & Yin, Dezhi & Liu, Zhanru, 2021. "Historical data-driven risk assessment of railway dangerous goods transportation system: Comparisons between Entropy Weight Method and Scatter Degree Method," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Min, Yun-Hong & Park, Myoung-Ju & Hong, Sung-Pil & Hong, Soon-Heum, 2011. "An appraisal of a column-generation-based algorithm for centralized train-conflict resolution on a metropolitan railway network," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 409-429, February.
    10. Nielsen, L.K. & Maróti, G., 2009. "Disruption Management of Rolling Stock in Passenger Railway Transportation," ERIM Report Series Research in Management ERS-2009-046-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Corman, F. & D’Ariano, A. & Pacciarelli, D. & Pranzo, M., 2012. "Optimal inter-area coordination of train rescheduling decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 71-88.
    12. Chang Han & Leishan Zhou & Zixi Bai & Wenqiang Zhao & Lu Yang, 2025. "An Integrated Approach to Schedule Passenger Train Plans and Train Timetables Economically Under Fluctuating Passenger Demands," Sustainability, MDPI, vol. 17(6), pages 1-31, March.
    13. Zhou, Xuesong & Zhong, Ming, 2005. "Bicriteria train scheduling for high-speed passenger railroad planning applications," European Journal of Operational Research, Elsevier, vol. 167(3), pages 752-771, December.
    14. Zhou, Xuesong & Zhong, Ming, 2007. "Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 320-341, March.
    15. Daganzo, Carlos F., 2007. "Corrigendum to "Urban gridlock: Macroscopic modeling and mitigation approaches" [Transportation Research Part B 41 (2007) 49-62]," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 379-379, March.
    16. Lawley, Mark & Parmeshwaran, Vijay & Richard, Jean-Philippe & Turkcan, Ayten & Dalal, Malay & Ramcharan, David, 2008. "A time-space scheduling model for optimizing recurring bulk railcar deliveries," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 438-454, June.
    17. Ghoseiri, Keivan & Szidarovszky, Ferenc & Asgharpour, Mohammad Jawad, 2004. "A multi-objective train scheduling model and solution," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 927-952, December.

    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:17:y:2025:i:6:p:2375-:d:1608045. 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.