Manufacturing systems involve human beings, machinery, non-linear dynamics, and a fusion of hierarchical and distributed organizational schemes. The cost-effective control and scheduling of these manufacturing systems require adaptability. Therefore, it is very important to implement competent on-line learning mechanisms that may accomplish balanced and adequate operation of processes with unknown dynamics in the manufacturing environment.
The integration of Dynamic Programming and Artificial Neural Networks has very important characteristics which may provide efficient real-time learning mechanisms for manufacturing. This thesis presents a system that achieves real-time learning using the mentioned integration for manufacturing scheduling. The system is capable of operational mappings. In addition, it utilizes reinforcement signals of the environment (a measure of how desirable the achieved state is taking into consideration the performance criteria) due to the lack of an expert scheduler. Conclusions are drawn and further research issues are discussed.