Abstract: The embodiments of the present invention provide a dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device.

Abstract: A deep reinforcement learning (DRL)-based intelligent job batching method and apparatus, and an electronic device are provided. The method includes: obtaining static features and a dynamic feature of each job, where the static features of the job include a delivery date, a specification and a process requirement of the job, and the dynamic feature of the job includes a receiving moment; and inputting the static features and the dynamic feature of each job into a job batching module, and using a Markov decision process (MDP) by the job batching module to combine jobs with similar features in a to-be-batched job set into an identical batch, so as to minimize a total quantity of batches obtained finally and a difference in features of jobs in each batch. The DRL-based intelligent job batching method and apparatus can learn a stable batching strategy and provide a stable and efficient job batching solution.

Abstract: The embodiments of the present invention provide a dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device.