In this paper, we investigate the optimal control problem of heavy haul train on steep downward slope. Aiming to reduce train's maintenance cost and, most importantly, to ensure operation safety, a multi-stage dynamic programming model is formulated with the minimized in-train forces criteria. To solve the proposed model, this paper introduces a Q-learning algorithm considering operational requirements and the characteristics of air braking. As the size of a standard lookup table increases exponentially, we improve the computational performance and solution quality by maintaining a bounded lookup table. The computational results demonstrate the effectiveness and performance of the proposed model and algorithm.


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    Titel :

    Optimal control of heavy haul train on steep downward slope


    Beteiligte:
    Xi Wang (Autor:in) / Tao Tang (Autor:in)


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    200528 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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