Abstract This paper proposes a route-based model for the Train Platforming and Rescheduling Problem (TPRP). Built on the concept of Degree of Conflict (DOC), the proposed model can accommodate various interlocking mechanisms with an aggregate railway yard representation. Thanks to the topology of a typical yard, such an aggregate representation promises to reduce the size of the optimization problems concerning yard operations. The TPRP model is formulated as a mixed integer linear program, and solved using both a commercial solver and two heuristic algorithms developed based on the idea of rolling horizon. The proposed model and algorithms are validated using several case studies constructed using data collected at a large high-speed railway station in China. We find the proposed TPRP model can produce, with reasonable computation resources, high quality platform/schedule decisions for real-world applications. In addition, the heuristic algorithms consistently offer high quality approximate solutions at a computational cost considerably lower than what is demanded by a benchmark commercial solver. The results from a simulation model show the differences between various interlocking mechanisms are well captured using a unified aggregate yard representation based on DOC. As expected, more flexible interlocking mechanisms can achieve greater operational efficiency at the expense of looser safety standards.

    Highlights An aggregate, route-based model for Train Platforming and Rescheduling Problem is proposed. A novel concept, Degree of conflict (DOC), is introduced to quantify inter-route conflicts. The model can accommodate flexible interlocking mechanisms through DOC. Two rolling horizon algorithms are proposed to solve the mixed integer linear model. The proposed model and algorithm are validated using numerical experiments based on real-world data.


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    Title :

    Train platforming and rescheduling with flexible interlocking mechanisms: An aggregate approach


    Contributors:
    Lu, Gongyuan (author) / Ning, Jia (author) / Liu, Xiaobo (author) / Nie, Yu (Marco) (author)


    Publication date :

    2022-01-15




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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