A learning-based methodology for dynamic scheduling in distributed manufacturing systems

SUMMARY

To enhance productivity in a distributed manufacturing system under hierarchical control, we develop a framework of dynamic scheduling scheme that explores routeing flexibility and handles uncertainties. We propose a learning-based methodology to extract scheduling knowledge for dispatching parts to machines. The proposed methodology includes three modules: discrete-event simulation, instance generation, and incremental induction. First, a sophisticated simulation module is developed to implement a dynamic scheduling scheme, to generate training examples, and to evaluate the methodology. Second, the search for training examples (good schedules) is successfully fulfilled by the genetic algorithm. Finally, we propose a tolerance-based learning algorithm that does not only acquire general scheduling rules from the training examples, but also adapts to any newly observed examples and thus facilitates knowledge modification. The experimental results show that the dynamic scheduling scheme significantly outperforms the static scheduling scheme with a single dispatching rule in a distributed manufacturing system.

Additional information

Notes on contributors

Y. YIH

To whom correspondence should be addressed at School of Industrial Engineering, Purdue University, 1287,Grissom Hall, West Lafayette, Indiana 47907-1287, USA.