A Container Transfer Scheduling Using Reinforcement Learning

Abstract

In container yard terminals, containers are brought by trucks in the random order. Since each container has its own destination and it cannot be moved after shipping, containers have to be loaded into a ship in a certain order. Therefore, containers require rearrangement from the randomly stacked initial state into desired order before shipping. In the problem, the number of states for the container stack increases by the exponential rate with increase of containers. In this paper, a new design method of reinforcement learning system is proposed to obtain desirable movements of containers and to reduce the run time for shipping. The proposed method assures that the optimal order of container movements can be obtained. Moreover, the method can reduce the required memory size. In order to show effectiveness of the proposed method, simulations for several examples are conducted. © 2003, The Institute of Electrical Engineers of Japan. All rights reserved.