@inproceedings{35cdb43674af43a4b694b4e01b0c0063,
title = "Dynamic Scheduling Method of Multi-objective Job Shop Based on Reinforcement Learning",
abstract = "Aiming at the dynamic scheduling problem in workshop production, we propose a multi-objective scheduling method. By analyzing the actual dynamic scheduling problem, a mathematical model is constructed. Then the dynamic interference factors in the actual production environment are classified, and the interference intensity and its parameters are designed. On this basis, a dynamic scheduling oriented process model is established by using reinforcement learning and scheduling rules, and the design of its state space, state action value table and reward function is introduced. Finally, the model is trained and we analyze the simulation results of different methods. The results show that the dynamic scheduling method based on reinforcement learning has good performance under different periods and disturbance intensity, which shows this method is effective and feasible for dynamic scheduling problem.",
keywords = "Dynamic disturbance, Dynamic scheduling, Multi-objective, Reinforcement learning, Scheduling rules",
author = "Zhenwei Zhang and Lihong Qiao and Zhicheng Huang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th China Conference on Intelligent Networked Things, CINT 2022 ; Conference date: 07-08-2022 Through 08-08-2022",
year = "2022",
doi = "10.1007/978-981-19-8915-5\_44",
language = "英语",
isbn = "9789811989148",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "510--524",
editor = "Lin Zhang and Wensheng Yu and Haijun Jiang and Yuanjun Laili",
booktitle = "Intelligent Networked Things - 5th China Conference, CINT 2022, Revised Selected Papers",
address = "德国",
}