Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

Abstract Dynamic selection of scheduling rules during real operations has been recognized as a promising approach to the scheduling of the production line. For this strategy to work effectively, sufficient knowledge is required to enable prediction of which rule is the best to use under the current line status. In this paper, a new learning algorithm for acquiring such knowledge is proposed. In this algorithm, a binary decision tree is automatically generated using empirical data obtained by iterative production line simulations, and it decides in real time which rule to be used at decision points during the actual production operations. The configuration of the developed dynamic scheduling system and the learning algorithm are described in detail. Simulation results on its application to the dispatching problem are discussed with regard to its scheduling performance and learning capability.