Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches seldom address machine selection i...Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches seldom address machine selection in the scheduling process. Composite rules, considering both machine selection andjob selection, are proposed in this paper. The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning (RL) algorithm. We define the conception of pressure to describe the system feature. Designing a reward function should be guided by the scheduling goal to accurately record the learning progress. Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.展开更多
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 Lagrangian relaxation is proposed to improv...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 Lagrangian 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 scheduling of manufacturing system. The efficiency and effectiveness of this method are verified by numerical experiments. 展开更多
文摘Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches seldom address machine selection in the scheduling process. Composite rules, considering both machine selection andjob selection, are proposed in this paper. The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning (RL) algorithm. We define the conception of pressure to describe the system feature. Designing a reward function should be guided by the scheduling goal to accurately record the learning progress. Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.
文摘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 Lagrangian 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 scheduling of manufacturing system. The efficiency and effectiveness of this method are verified by numerical experiments.
