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A logic-based Benders decomposition for microscopic railway timetable planning

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  • Leutwiler, Florin
  • Corman, Francesco

Abstract

Railway timetable planning is one of the key factors in the successful operation of a railway network. The timetable must satisfy all operational restrictions at a microscopic representation of the railway network, while maximizing transportation capacity for passengers and freight. The microscopic planning of a railway timetable is an NP-Hard problem, difficult to solve for large-scale railway networks, such as those of entire countries. In this work, we propose a logic Benders decomposition approach to solve the problem of microscopic railway timetable planning. Our decomposition exploits the typical structure of a railway with dense networks around major hubs and sparse connections in-between hubs. A logic Benders cut is designed, which we are able to compute effectively for all decomposed problems within our considered structure, using a SAT based algorithm we developed. Moreover, an aggregation scheme for Benders cuts is proposed to speed up the iterative process. Experiments on real-world cases of the Swiss Federal Railways show a clear improvement in scalability compared to a variety of benchmarks including centralized approaches.

Suggested Citation

  • Leutwiler, Florin & Corman, Francesco, 2022. "A logic-based Benders decomposition for microscopic railway timetable planning," European Journal of Operational Research, Elsevier, vol. 303(2), pages 525-540.
  • Handle: RePEc:eee:ejores:v:303:y:2022:i:2:p:525-540
    DOI: 10.1016/j.ejor.2022.02.043
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    References listed on IDEAS

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    7. Leonardo Lamorgese & Carlo Mannino & Mauro Piacentini, 2016. "Optimal Train Dispatching by Benders’-Like Reformulation," Transportation Science, INFORMS, vol. 50(3), pages 910-925, August.
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    Cited by:

    1. Guo, Penghui & Zhu, Jianjun, 2023. "Capacity reservation for humanitarian relief: A logic-based Benders decomposition method with subgradient cut," European Journal of Operational Research, Elsevier, vol. 311(3), pages 942-970.
    2. Oddvar Kloster & Bjørnar Luteberget & Carlo Mannino & Giorgio Sartor, 2023. "An Optimization-Based Decision Support Tool for Incremental Train Timetabling," SN Operations Research Forum, Springer, vol. 4(3), pages 1-20, September.
    3. Yin, Jiateng & Pu, Fan & Yang, Lixing & D’Ariano, Andrea & Wang, Zhouhong, 2023. "Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).

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