A dynamic job shop scheduling method based on lagrangian relaxation

Due to the complexity of dynamic job shop scheduling in flexible manufacturing system(FMS), many heuristic rules are still used today. A dynamic scheduling approach based on Lagrangi an relaxation is proposed to improve the quality and guarantee the real time capability of dynamic scheduling. The proposed method makes use of the dynamic predictive optimal theory combined with Lagrangian relaxation to obtain a good solution that can be evaluated quantitatively. The Lagrangian multipliers introduced here are capable of describing machine predictive states and system capacity constraints. This approach can evaluate the suboptimality of the scheduling systems. It can also quickly obtain high quality feasible schedules, thus enabling Lagrangian relaxation to be better used in the dynamic seheduling of manufacturing system. The efficieney and effectiveness of this method are verified by numerical experiments.