Production scheduling is critical to manufacturing system.Dispatching rules are usually applied dynamically to schedule (?)he job in a dynamic job-shop.Existing scheduling approaches sel- dom address machine selection in the scheduling process.Composite rules,considering both machine selection and job 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 concep- tion 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. Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule (?) He job in a dynamic job-shop. Existing scheduling approaches sel-dom address machine selection in the scheduling process. Complex rules, job 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 concep- tion 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.