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A column generation based approach for the Train Network Design Optimization problem

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  • Jin, Jian Gang
  • Zhao, Jun
  • Lee, Der-Horng

Abstract

This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently.

Suggested Citation

  • Jin, Jian Gang & Zhao, Jun & Lee, Der-Horng, 2013. "A column generation based approach for the Train Network Design Optimization problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 1-17.
    • Handle: RePEc:eee:transe:v:50:y:2013:i:c:p:1-17
    DOI: 10.1016/j.tre.2012.11.004
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    Cited by:

    1. Yi Tao & Ek Peng Chew & Loo Hay Lee & Yuran Shi, 2017. "A column generation approach for the route planning problem in fourth party logistics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 165-181, February.
    2. Zheng, Hankun & Sun, Huijun & Kang, Liujiang & Dai, Peiling & Wu, Jianjun, 2023. "Multi-route coordination for bus systems in response to road disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    3. Khaled, Abdullah A. & Jin, Mingzhou & Clarke, David B. & Hoque, Mohammad A., 2015. "Train design and routing optimization for evaluating criticality of freight railroad infrastructures," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 71-84.
    4. Chen, Chongshuang & Dollevoet, Twan & Zhao, Jun, 2018. "One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 1-30.
    5. 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).
    6. Alena Otto & Erwin Pesch, 2017. "Operation of shunting yards: train-to-yard assignment problem," Journal of Business Economics, Springer, vol. 87(4), pages 465-486, May.
    7. Chen, C. & Dollevoet, T.A.B. & Zhao, J., 2017. "One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm," Econometric Institute Research Papers EI-2017-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Meng, Lingyun & Zhou, Xuesong, 2019. "An integrated train service plan optimization model with variable demand: A team-based scheduling approach with dual cost information in a layered network," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 1-28.
    9. 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.
    10. Alena Otto & Erwin Pesch, 2019. "The train-to-yard assignment problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 549-580, June.

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