A simulation based learning meachanism for scheduling systems with continuous control and update structure

A simulation based learning mechanism is proposed in this study. The system learns in the manufacturing environment by constructing a learning tree and selects a dispatching rule from the tree for each scheduling period. The system utilizes the process control charts to monitor the performance of the learning tree which is automatically updated whenever necessary. Therefore, the system adapts itself for the changes in the manufacturing environment and works well over time. Extensive simulation experiments are conducted for the system parameters such as monitoring (MPL) and scheduling period lengths (SPL) on a job shop problem with objective of minimizing average tardiness. Simulation results show that the performance of the proposed system is considerably better than the simulation-based single-pass and multi-pass scheduling algorithms available in the literature.