Development of an Intelligent Scheduling System for a Semiconductor Foundry Fab

With the selection of the real-time salient information of machines and parts and then a rule’s dispatching mechanism is built for the scheduling task, the dynamic scheduling rules would outperform static ones in a flexible manufacturing system (FMS). Scheduling plays an important role in the production control in a foundry fab, which can be seen as a huge FMS. For a dynamic scheduling system, two critical issues dominate the performance of a scheduled FMS, one is the selection of system attributes and the other is the design of the dispatching mechanism, namely, the classifier design. This thesis proposes a self-organizing scheduling model (SOSM) aiming to provide a FMS with the optimal attribute selection and high generalization, high-accuracy classifier. The proposed scheduling model combines several intelligent methods to achieve these goals, including fuzzy set theory, genetic algorithms, and neural networks. A typical FMS model is conducted in the experiments for the demonstration. Experimental results show that the SOSM outperforms the static dispatching rules under three different performance criteria.