[1]韩忠华,黎恺嘉,周晓锋,等.基于深度学习的柔性流水车间排产优化问题研究[J].智能系统学报,2023,18(3):468-479.[doi:10.11992/tis.202112028]
HAN Zhonghua,LI Kaijia,ZHOU Xiaofeng,et al.Research on the optimization of flexible flow shop scheduling based on deep learning[J].CAAI Transactions on Intelligent Systems,2023,18(3):468-479.[doi:10.11992/tis.202112028]
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HAN Zhonghua,LI Kaijia,ZHOU Xiaofeng,et al.Research on the optimization of flexible flow shop scheduling based on deep learning[J].CAAI Transactions on Intelligent Systems,2023,18(3):468-479.[doi:10.11992/tis.202112028]
基于深度学习的柔性流水车间排产优化问题研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第3期
页码:
468-479
栏目:
学术论文—机器学习
出版日期:
2023-07-05
- Title:
- Research on the optimization of flexible flow shop scheduling based on deep learning
- Keywords:
- deep learning; production scheduling optimization; processing on-line sequence; flexible flow shop; gated recurrent unit; sequence-to-sequence; attention mechanism; historical production data
- 分类号:
- TP183
- 摘要:
- 求解柔性流水车间排产优化问题的全局优化算法常采用群体进化算法或数学规划算法，但对新的投产任务进行排产优化过程中，这些优化方法每次都需重新进行耗时的迭代寻优计算，因此提出了一种基于深度学习的智能排产优化方法，通过历史生产数据训练基于门控循环单元构建的序列到序列深度学习模型，重点研究排产数据中生产任务信息、工艺信息与排产结果的相关性，并将其作为模型编码器的输入；模型解码器的输出为工件的上线序，依据该上线序可以快速给出有效的排产结果，并通过引入注意力机制进一步提高寻优的精度和速率。仿真实验结果表明，基于深度学习的柔性流水车间排产优化方法可以快速获取较好的排产优化结果。
- Abstract:
- The global optimization algorithm for solving the production scheduling optimization problem of the flexible flow shops often adopts the group evolution algorithm or the mathematical programming algorithm. In the process of scheduling and optimizing new production tasks, these optimization methods need to perform time-consuming iterative optimization calculations again every time. Therefore, an intelligent scheduling optimization method based on deep learning is proposed to solve above problem. Through historical production data training, a sequence-to-sequence (Seq2Seq) deep learning model based on gated recurrent unit (GRU) is used to focus on the correlation between the production task information and process information in the scheduling data and the scheduling results. And further this correlation is used as the input of the model encoder; and the output of the model decoder is the on-line sequence of the workpiece. According to the on-line sequence, effective scheduling results can be quickly given, and the accuracy and speed of optimization can be further improved by introducing an attention mechanism. The results of simulation experiment show that the flexible flow shop scheduling optimization method based on deep learning can quickly obtain better scheduling optimization results.
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备注/Memo
收稿日期:2021-12-15。
基金项目:国家自然科学基金项目（61873174）；辽宁省重点研发计划项目（2020JH2/10100039）；辽宁省教育厅高等学校基本科研项目重点项目（LJKZ0583）；沈阳市科技项目（Z18-5-015）.
作者简介:韩忠华,教授,博士,主要研究方向为生产运作管理、企业自动化系统集成技术、车间排产与生产调度算法工程应用。主持和参与国家级、省部级科研项目24项,获知识产权共16项,参与编制国家标准2项。发表学术论文43篇;黎恺嘉,硕士研究生,主要研究方向为生产优化、深度学习;周晓锋,副研究员,博士,主要研究方向为工业大数据分析。
通讯作者:黎恺嘉.E-mail:lkj199703@163.com
更新日期/Last Update:
1900-01-01