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Scheduling and Evaluation of a Power-Concentrated EMU on a Conventional Intercity Railway Based on the Minimum Connection Time

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  • Yinan Wang

    (Postgraduate Department, China Academy of Railway Science, Beijing 100081, China)

  • Limin Xu

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Xiao Yang

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Jingjing Bao

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Feng Lin

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Yiwei Guo

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
    China Railway Train Working Diagram Technology Center, Beijing 100081, China)

  • Yixiang Yue

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

Abstract

Power-concentrated EMU trains have the advantages of being fast and comfortable, having a flexible formation and a short turn-back time, and so on. They can effectively release the transportation capacity of tense lines and hubs (the replacement of conventional trains with power-concentrated EMUs can reduce the time it takes to enter and exit locomotive yards by 40 min per train), optimize operating structures, improve the quality and efficiency of passenger products for conventional railways, and enhance the travel experience of passengers. Moreover, they have certain cost advantages and practical operational value for improving the market competitiveness of conventional railways. In this study, a two-stage, two-layer cycle method is adopted to solve the application plan of an EMU with the minimum total connection time. Through the decomposition of optimization objectives, the search space and the solution scale in each stage are reduced. In the first stage, the feasible number of routes and the number division plan of internal running lines are listed. In the second stage, an improved ant colony algorithm is designed to arrange and combine the internal running lines in each plan to improve the search quality and convergence speed, which changes the pheromone volatilization coefficient with iteration. The optimal number of routes, the number of internal routes, and the optimal sequence between routes are obtained. The study also puts forward a method of route division according to the passenger load factor, which can help railway bureaus adjust the capacity according to fluctuations in demand. A running diagram of six pairs of power-concentrated EMUs on an intercity railway is used as the background to solve the problem. The optimal connection plan with 14 groups of different route division plans was evaluated by using the entropy weight–TOPSIS method, and the optimal plan was obtained in the form of a route division method with two groups of routes with three pairs of trains in each group. Compared with the actual operation plan, the number of routes and the number of first-level repairs are reduced by 50%, respectively, which can effectively reduce the operation and maintenance costs of EMUs. Compared with the actual plan, the average operation mileage is increased by 100%, the average mileage loss is decreased by 54.6%, and the minimum distance traveled by EMUs is increased by 200%, which indicates that the mileage maintenance cycle of the actual operation plan is not fully used. The average number of tasks of EMUs is increased by 100%, indicating that the efficiency of EMUs in the actual operation plan needs to be improved. The traffic mileage balance is improved by 100%, indicating that the EMUs in different routes are more balanced.

Suggested Citation

  • Yinan Wang & Limin Xu & Xiao Yang & Jingjing Bao & Feng Lin & Yiwei Guo & Yixiang Yue, 2025. "Scheduling and Evaluation of a Power-Concentrated EMU on a Conventional Intercity Railway Based on the Minimum Connection Time," Mathematics, MDPI, vol. 13(3), pages 1-32, February.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:508-:d:1582938
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    References listed on IDEAS

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    1. Valentina Cacchiani & Alberto Caprara & Paolo Toth, 2019. "An Effective Peak Period Heuristic for Railway Rolling Stock Planning," Transportation Science, INFORMS, vol. 53(3), pages 746-762, May.
    2. Arianna Alfieri & Rutger Groot & Leo Kroon & Alexander Schrijver, 2006. "Efficient Circulation of Railway Rolling Stock," Transportation Science, INFORMS, vol. 40(3), pages 378-391, August.
    3. Ralf Borndörfer & Markus Reuther & Thomas Schlechte & Kerstin Waas & Steffen Weider, 2016. "Integrated Optimization of Rolling Stock Rotations for Intercity Railways," Transportation Science, INFORMS, vol. 50(3), pages 863-877, August.
    4. Erwin Abbink & Bianca van den Berg & Leo Kroon & Marc Salomon, 2004. "Allocation of Railway Rolling Stock for Passenger Trains," Transportation Science, INFORMS, vol. 38(1), pages 33-41, February.
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