This paper proposes a new marshaling method for assembling an outgoing train. In the proposed method, each set of freight cars that have the same destination make a group, and the desirable group layout constitutes the best outgoing train. The incoming freight cars are classified into several ``sub-tracks'' searching better assignment in order to reduce the transfer distance of locomotive. Classifications and marshaling plans based on the transfer distance of a locomotive are obtained autonomously by a reinforcement learning system. Then, the number of sub-tracks utilized in the classification is determined by the learning system in order to yield generalization capability.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A New Reinforcement Learning for Train Marshaling with Generalization Capability


    Contributors:

    Published in:

    Publication date :

    2014-06-19


    Size :

    5 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Commutation circuit, flexible marshaling unit and flexible marshaling method

    WU MINGYANG / ZHANG CHUNYU / GAO CHUNHAI | European Patent Office | 2022

    Free access

    Train control system compatible method based on virtual marshaling

    OU DONGXIU / JI YUQING / LIU YIXIAO et al. | European Patent Office | 2022

    Free access


    Online coupling method and online un-marshaling method for train

    CHEN YI / ZHENG WANYUN / XIAO MENG et al. | European Patent Office | 2020

    Free access