This paper proposes a new marshaling method for assembling an outgoing train considering the layout of incoming freight cars as well as the outgoing ones. In the addressed problem, freight cars are initially located in a incomming train by the random layout, and they are rearranged and lined into a main track in a certain desired order for an outgoing train. In the proposed method, each set of freight cars that have the same destination make a group, and the desired group layout constitutes the best outgoing train. Then, the incoming freight cars are classified into several “sub-tracks” searching better assignment in order to reduce the total processing time. Classifications and marshaling plans based on the processing time are obtained by a reinforcement learning system. 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. The total processing time is reduced by obtaining simultaneously the classification of incoming cars, 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.
An Intelligent Train Marshaling Plan of Freight Cars Considering I/O Arrangements
Lect. Notes Electrical Eng.
2013-12-01
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Container transfer problem , Freight train , Marshaling , Q-learning , Reinforcement learning , Scheduling Engineering , Mathematical and Computational Engineering , Control, Robotics, Mechatronics , Mathematical Modeling and Industrial Mathematics , Computational Intelligence , Theoretical, Mathematical and Computational Physics
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