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首页> 外文期刊>IAENG Internaitonal journal of computer science >On Reinforcement Learning Methods for Generating Train Marshaling Plan Considering Group Layout of Freight Cars
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On Reinforcement Learning Methods for Generating Train Marshaling Plan Considering Group Layout of Freight Cars

机译:考虑货车组布局的列车编组计划的强化学习方法

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摘要

This paper proposes a new reinforcement learning method for train marshaling. In the proposed method, marshaling plans for freight cars in a train are generated based on the processing time. In order to evaluate the processing time, the total transfer distance of a locomotive and the total movement counts of freight cars are simultaneously considered. Moreover, by grouping freight cars that have the same destination, candidates of the desired arrangement of the outbound train is extended. This feature is considered in the learning algorithm, so that the total processing time is reduced. Then, the order of movements of freight cars, the position for each removed car, the layout of groups in a train, the arrangement of cars in a group and the number of cars to be moved are simultaneously optimized to achieve minimization of the total processing time for obtaining the desired arrangement of freight cars for an outbound train. Initially, freight cars are located in a freight yard by the random layout, and they are moved and lined into a main track in a certain desired order in order to assemble an out bound train. In computer simulations, a perfoemance of the proposed method is compared to that of the method based on the movement counts of freight cars and of the method based on the transfer distance of locomotive.
机译:本文提出了一种新的列车编组强化学习方法。在提出的方法中,根据处理时间生成火车中货车的编组计划。为了评估处理时间,同时考虑了机车的总转移距离和货车的总运动次数。此外,通过将具有相同目的地的货运车辆分组,扩展了出站列车的期望布置的候选者。在学习算法中考虑了此功能,因此减少了总处理时间。然后,同时优化货车的移动顺序,每辆移出的货车的位置,火车中的组的布局,组中的车组布置以及要移动的车组数量,以实现总处理量的最小化时间来获得用于出站列车的货车的所需布置。最初,货运车以随机布局放置在货运场中,然后将它们以某种所需的顺序移动并排入主轨道,以组装出站列车。在计算机仿真中,将所提方法的性能与基于货车运动次数的方法以及基于机车转移距离的方法的性能进行了比较。

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