公开(公告)号：US20230067605A1

公开(公告)日：2023-03-02

申请号：US17456581

申请日：2021-11-25

Applicant: Beijing University of Posts and Telecommunications

Inventor： Liang LIU ,  Xiaolong ZHENG ,  Huadong MA ,  Chengling JIANG ,  Zihui LUO

IPC: G06N3/08 ,  G06N3/04

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.