A New Method for Marshaling Plan Using a Reinforcement Learning Considering Desired Layout of Containers in Terminals
| Authors: | Hirashima Yoichi, Okayama University, Japan
Furuya Osamu, Okayama University, Japan
Takeda Kazuhiro, Mitsubishi Heavy Industries, Japan
Deng Mingcong, Okayama University, Japan
Inoue Akira, Okayama University, Japan |
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| Topic: | 1.2 Adaptive and Learning Systems |
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| Session: | Learning and Intelligent Control |
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| Keywords: | Scheduling, Intelligence, Learning system, Industry automation, Reinforcement learning |
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Abstract
In container yard terminals, containers brought by trucks in the random order. Containers have to be loaded into the ship in a certain order, since each container has its own shipping destination and it cannot be rearranged after loading. Therefore, containers have to be rearranged from the initial arrangement into the desired arrangement before shipping. In this paper, a Q-Learning algorithm considering rearrangement destination of containers for a marshaling in the container yard terminal is proposed. In the proposed method, the learning process consists of two parts: rearrangement order of each container is learned explicitly as well as considerd in the learning algorithm, so that the learning performance can be improved. In order to show effectiveness of the proposed method, simulations for several examples are conducted.
