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A New Resource-Constrained Multicommodity Flow Model for Conflict-Free Train Routing and Scheduling

Author

Listed:
  • G. Caimi

    (Institute for Operations Research, ETH Zurich, 8092 Zurich, Switzerland)

  • F. Chudak

    (D-Wave Systems Inc., Burnaby, British Columbia V5C 6G9, Canada)

  • M. Fuchsberger

    (Institute for Operations Research, ETH Zurich, 8092 Zurich, Switzerland)

  • M. Laumanns

    (Institute for Operations Research, ETH Zurich, 8092 Zurich, Switzerland)

  • R. Zenklusen

    (Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

This paper addresses the problem of generating conflict-free train schedules on a microscopic model of the railway infrastructure. Conflicts arise if two or more trains are scheduled to block the same track section at the same time. A standard model for this problem is the so-called conflict graph, where each considered train path corresponds to a vertex, and edges represent pairwise conflicts so that a conflict-free schedule corresponds to a maximum independent set. Because the linear programming relaxation of the conflict graph formulation is typically very weak, we develop an alternative model using the sequence of resources that each train path passes, encoded in a resource tree. For each resource, we can efficiently determine the maximal conflict cliques by scanning through the blocking times of all train paths and use these cliques as strong cutting planes in an integer linear programming formulation. We show that the number of maximal conflict cliques is linear in the number of train paths, so the ILP formulation uses much fewer but stronger constraints compared to the conflict graph model. In tests with real-world data from the Swiss Federal Railways, the new Resource Tree Conflict Graph model generates for major stations within seconds, even though the underlying model contains about half a million binary variables. This corresponds to a reduction of the computation time of roughly two orders of magnitude when compared to previous approaches and thus allows us to tackle considerable larger problem instances.

Suggested Citation

  • G. Caimi & F. Chudak & M. Fuchsberger & M. Laumanns & R. Zenklusen, 2011. "A New Resource-Constrained Multicommodity Flow Model for Conflict-Free Train Routing and Scheduling," Transportation Science, INFORMS, vol. 45(2), pages 212-227, May.
  • Handle: RePEc:inm:ortrsc:v:45:y:2011:i:2:p:212-227
    DOI: 10.1287/trsc.1100.0349
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    References listed on IDEAS

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    Cited by:

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    5. Khodakaram Salimifard & Sara Bigharaz, 2022. "The multicommodity network flow problem: state of the art classification, applications, and solution methods," Operational Research, Springer, vol. 22(1), pages 1-47, March.
    6. Wenliang Zhou & Wenzhuang Fan & Xiaorong You & Lianbo Deng, 2019. "Demand-Oriented Train Timetabling Integrated with Passenger Train-Booking Decisions," Sustainability, MDPI, vol. 11(18), pages 1-34, September.
    7. Sels, P. & Dewilde, T. & Cattrysse, D. & Vansteenwegen, P., 2016. "Reducing the passenger travel time in practice by the automated construction of a robust railway timetable," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 124-156.
    8. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    9. Twan Dollevoet & Dennis Huisman & Leo Kroon & Marie Schmidt & Anita Schöbel, 2015. "Delay Management Including Capacities of Stations," Transportation Science, INFORMS, vol. 49(2), pages 185-203, May.
    10. Ghaemi, Nadjla & Cats, Oded & Goverde, Rob M.P., 2017. "A microscopic model for optimal train short-turnings during complete blockages," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 423-437.
    11. Leonardo Lamorgese & Carlo Mannino & Mauro Piacentini, 2016. "Optimal Train Dispatching by Benders’-Like Reformulation," Transportation Science, INFORMS, vol. 50(3), pages 910-925, August.
    12. Zhang, Yongxiang & D'Ariano, Andrea & He, Bisheng & Peng, Qiyuan, 2019. "Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 237-278.
    13. Zhou, Wenliang & Teng, Hualiang, 2016. "Simultaneous passenger train routing and timetabling using an efficient train-based Lagrangian relaxation decomposition," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 409-439.
    14. Samà, Marcella & Pellegrini, Paola & D’Ariano, Andrea & Rodriguez, Joaquin & Pacciarelli, Dario, 2016. "Ant colony optimization for the real-time train routing selection problem," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 89-108.
    15. Rajnish Kumar & Goutam Sen & Samarjit Kar & Manoj Kumar Tiwari, 2018. "Station Dispatching Problem for a Large Terminal: A Constraint Programming Approach," Interfaces, INFORMS, vol. 48(6), pages 510-528, November.
    16. Nikola Bešinović & Rob M. P. Goverde, 2019. "Stable and robust train routing in station areas with balanced infrastructure capacity occupation," Public Transport, Springer, vol. 11(2), pages 211-236, August.
    17. Wang, Dian & D’Ariano, Andrea & Zhao, Jun & Zhong, Qingwei & Peng, Qiyuan, 2022. "Integrated rolling stock deadhead routing and timetabling in urban rail transit lines," European Journal of Operational Research, Elsevier, vol. 298(2), pages 526-559.
    18. Pellegrini, Paola & Rodriguez, Joaquin, 2013. "Single European Sky and Single European Railway Area: A system level analysis of air and rail transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 64-86.
    19. Pellegrini, Paola & Marlière, Grégory & Rodriguez, Joaquin, 2014. "Optimal train routing and scheduling for managing traffic perturbations in complex junctions," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 58-80.
    20. Jiateng Yin & Lixing Yang & Andrea D’Ariano & Tao Tang & Ziyou Gao, 2022. "Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3234-3258, November.
    21. Fu, Huiling & Nie, Lei & Meng, Lingyun & Sperry, Benjamin R. & He, Zhenhuan, 2015. "A hierarchical line planning approach for a large-scale high speed rail network: The China case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 61-83.
    22. Lu Yang & Leishan Zhou & Hanxiao Zhou & Chang Han & Wenqiang Zhao, 2023. "A Lagrangian Method for Calculation of Passing Capacity on a Railway Hub Station," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    23. Dewilde, Thijs & Sels, Peter & Cattrysse, Dirk & Vansteenwegen, Pieter, 2014. "Improving the robustness in railway station areas," European Journal of Operational Research, Elsevier, vol. 235(1), pages 276-286.

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