Abstract
In this paper, we apply a neural network to the real-time scheduling of a semiconductor manufacturing system. Because it is very difficult to train the neural network using large-scale semiconductor manufacturing systems, we use a small-scale model of the manufacturing system for training. In order to compensate for the discrepancy between the models, a target-cycle-time tunable after training is used for scheduling. Numerical experiments show that the proposed method outperforms the dispatching rule proposed in the literature.
