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Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: A column-generation-based approach

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  • Pan, Hanchuan
  • Yang, Lixing
  • Liang, Zhe

Abstract

Train timetabling and rolling stock circulation planning are two important optimization problems that arise in the daily operations of urban rail transit lines. This study proposes a novel approach to simultaneously optimize the train timetable and rolling stock circulation plan with flexible train compositions on an urban rail transit line in order to better match the passenger demand and supplied transport capacity, and further improve operation flexibility and extract gains in efficiency, in which the dynamic passenger demand is particularly considered in the formulation process. We first propose two integer linear programming models that are formulated on time-space networks by considering flexible coupling/splitting activities. The models consist of restricted master problems for optimizing the total generalized cost and subproblems that are used to generate different train composition routes. An effective column-generation-based diving heuristic algorithm is developed to solve the problem of interest, and it is able to simultaneously obtain the train timetable and rolling stock circulation plan. Several sets of experimental scenarios, involving relatively small-scale cases and a case derived from real-world Shanghai Metro data, are tested to verify the effectiveness of the proposed model and algorithm.

Suggested Citation

  • Pan, Hanchuan & Yang, Lixing & Liang, Zhe, 2023. "Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: A column-generation-based approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 184-206.
    • Handle: RePEc:eee:ejores:v:305:y:2023:i:1:p:184-206
    DOI: 10.1016/j.ejor.2022.05.039
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    Cited by:

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    2. Weiya Chen & Qinyu Zhuo & Lu Zhang, 2023. "Modeling and Heuristically Solving Group Train Operation Scheduling for Heavy-Haul Railway Transportation," Mathematics, MDPI, vol. 11(11), pages 1-15, May.
    3. Chai, Simin & Yin, Jiateng & D’Ariano, Andrea & Liu, Ronghui & Yang, Lixing & Tang, Tao, 2024. "A branch-and-cut algorithm for scheduling train platoons in urban rail networks," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    4. Pan, Hanchuan & Yang, Lixing & Liang, Zhe & Yang, Hai, 2024. "New Exact Algorithm for the integrated train timetabling and rolling stock circulation planning problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 316(3), pages 906-929.
    5. Xu, Guangming & Zhong, Linhuan & Liu, Wei & Guo, Jing, 2024. "A flexible train composition strategy with extra-long trains for high-speed railway corridors with time-varying demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).

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