现代制造工程Issue(2)：10-16,7.DOI:10.16731/j.cnki.1671-3133.2025.02.002
深度强化学习求解动态柔性作业车间调度问题
A dynamic flexible job shop scheduling method based on deep reinforcement learning
摘要
Abstract
The study of the artificial intelligence algorithms for job shop scheduling has gained attention due to the advancements in intelligent manufacturing technologies like smart factories.Dynamic events in the job shop are crucial factors affecting schedu-ling effectiveness.To this end,it proposes a novel approach employing the deep reinforcement learning to solve the dynamic flexi-ble job shop scheduling problem with random job arrival.Initially,a mathematical model is formulated for the dynamic job shop scheduling problem with the objective of minimizing the total tardiness.Subsequently,eight job shop state features are extracted,and six composite scheduling rules are designed.An ε-greedy action selection strategy is adopted,and the reward function is de-signed.Finally,the advanced D3QN algorithm is introduced to solve the problem and the effectiveness of this method is verified on different scale of instances.The results show that the D3QN algorithm effectively solves the dynamic flexible job shop schedu-ling problem with random job arrival,and the winning rate in all instances is 58.3％.Compared with traditional DQN and DDQN algorithm,the total tardiness is reduced by 11.0％and 15.4％respectively,which proves that this method further enhances the production efficiency of the job shop.关键词
深度强化学习/D3QN算法/工件随机抵达/柔性作业车间调度/动态调度Key words
deep reinforcement learning/D3QN algorithm/random job arrival/flexible job shop scheduling problem/dynamic scheduling分类
信息技术与安全科学引用本文复制引用
杨丹,舒先涛,余震,鲁光涛,纪松霖,王家兵..深度强化学习求解动态柔性作业车间调度问题[J].现代制造工程,2025,(2):10-16,7.基金项目
国家自然科学基金项目(51808417) （51808417）
