摘要
为了提高连续生产流水线的调度效果,提升生产线的加工效率,提出连续生产流水线深度强化学习优化调度算法。首先,结合蒙特卡罗算法和贝叶斯评估方法降低连续生产线流水线问题的数据复杂度;其次,采用深度神经网络模型优化流水线调度参数,对其进行评估及编码;最后,将迭代贪婪算法与深度强化学习方法结合,对调度数据问题实施模型求解,实现连续生产流水线调度。试验结果表明:本文算法的调度结果最优,综合评价结果均高于0.9531,工序延时优化至5 min以下,收敛速度较快,提升了生产线的加工效率。
In order to improve the scheduling effect of the continuous production line and improve the processing efficiency of the production line,a deep reinforcement learning optimization scheduling algorithm for the continuous production line is proposed.Combining Monte Carlo algorithm and Bayesian evaluation method to reduce the data complexity of the continuous production line problem;A deep neural network model is used to optimize the pipeline scheduling parameters,evaluate and code them;The iterative greedy algorithm is combined with the deep reinforcement learning method to solve the scheduling data problem and realize the continuous production line scheduling.The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are higher than 0.9531,and the process delay is optimized to less than 5 min,which improves the processing efficiency of the production line.
作者
朱广贺
朱智强
袁逸萍
ZHU Guang-he;ZHU Zhi-qiang;YUAN Yi-ping(College of Computer Science and Technology,Xinjiang Normal University,Urumqi 830000,China;College of Software Engineering,Xinjiang University,Urumqi 830000,China;College of Mechanical Engineering,Xinjiang University,Urumqi 830000,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第7期2086-2092,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(71961029)。
关键词
深度强化学习
流水线生产
调度优化
迭代贪婪算法
数据降维
deep reinforcement learning
assembly line production
scheduling optimization
iterative greedy algorithm
data dimension reduction
