Author:
Yoichi Hirashima
Affiliation:
Fac. Information Science and Technology, Osaka Institute of Technology, Japan
Keyword(s):
Container marshaling, Block stacking problem, Q-learning, Reinforcement learning, Binary tree.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Decision Support Systems
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
This paper proposes a new scheduling method for a marshaling in the container yard terminal. The proposed method is derived based on Q-Learning algorithm considering the desired position of containers that are to be loaded into a ship. In the method, 3 processes can be optimized simultaneously: rearrangement order of containers, layout of containers assuring explicit transfer of container to the desired position, and removal plan for preparing the rearrange operation. Moreover, the proposed method generates several desired positions for each container, so that the learning performance of the method can be improved as compared to the conventional methods. In general, at container yard terminals, containers are stacked in the arrival 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 the problem, the number of container-arrangements increases by the exponential rate with increase of total count of containers, and the rearrangement process occupies large part of total run time of material handling operation at the terminal. For this problem, conventional methods require enormous time and cost to derive an admissible result. In order to show effectiveness of the proposed method, computer simulations for several examples are conducted.
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