A self-adaptive agent-based fuzzy-neural scheduling system for a wafer fabrication factory

A self-adaptive agent-based fuzzy-neural system is constructed in this study to enhance the performance of scheduling jobs in a wafer fabrication factory. The system integrates dispatching, performance evaluation and reporting, and scheduling policy optimization. Unlike in the past studies a single pre-determined scheduling algorithm is used for all agents, in this study every agent develops and modifies its own scheduling algorithm to adapt it to the local conditions. To stabilize the performance of the self-adaptive agent-based fuzzy-neural scheduling system, some treatments have also been taken. To evaluate the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation is also applied in this study to generate some test data. According to experimental results, the self-adaptive agent-based fuzzy-neural system did improve the performance of scheduling jobs in the simulated wafer fabrication factory, especially with respect to the average cycle time and cycle time standard deviation.