An on-line production scheduler using neural network and simulator based on manufacturing system states

Customers are demanding shorter lead times and higher product variety without making concessions on product price and quality. To remain competitive, a manufacturing system needs to react adequately to perturbations on its environment and uncertainties in manufacturing processes. The paper touches upon three research topics for the development of a scheduler based on manufacturing system states: development of a simulator for the simulation of a manufacturing system, the clustering method for manufacturing system states, and the search method for the most compatible dispatching rule to a manufacturing system state. Finally, the results of simulation experiments are given to compare the proposed method with other scheduling methods. The result shows that the superiority of the proposed scheduler. In the process of developing the scheduler, a general methodology for the development of a simulator and the clustering method for system states were developed. The proposed methodology for the development of simulators seems to be useful for developing simulators for various domains. The clustering method of system states and the knowledge acquisition method for scheduling rules are shown to be efficient for the development of an autonomous real-time scheduling system.