Intelligent assembly scheduling for large laser devices

Xiong Zhao; Yin Lingyu; Pei Guoqing; Wang Chengcheng; Zhou Hai

doi:10.11884/HPLPB202335.230170

Volume 35 Issue 9

Sep. 2023

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Article Navigation > High Power Laser and Particle Beams > 2023 > 35(9): 092002

Wang Feng, Zhang Xing, Li Yulong, et al. Progress in high time- and space-resolving diagnostic technique for laser-driven inertial confinement fusion[J]. High Power Laser and Particle Beams, 2020, 32: 112002. doi: 10.11884/HPLPB202032.200136

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Xiong Zhao, Yin Lingyu, Pei Guoqing, et al. Intelligent assembly scheduling for large laser devices[J]. High Power Laser and Particle Beams, 2023, 35: 092002. doi: 10.11884/HPLPB202335.230170

Citation:

Xiong Zhao, Yin Lingyu, Pei Guoqing, et al. Intelligent assembly scheduling for large laser devices[J]. High Power Laser and Particle Beams, 2023, 35: 092002. doi: 10.11884/HPLPB202335.230170

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Intelligent assembly scheduling for large laser devices

doi: 10.11884/HPLPB202335.230170

Laser Fusion Research Center, CAEP, Mianyang 621900, China

Received Date: 2023-06-06
Accepted Date: 2023-08-25
Rev Recd Date: 2023-08-25

Available Online: 2023-08-31

Publish Date: 2023-09-15

Abstract

Abstract

Aiming at the assembly scheduling problem of optical and mechanical modules for large laser devices, a scheduling priority rule acquisition method based on artificial neural networks (ANNs) is proposed. In the offline phase, this method optimizes the scheduling data through genetic algorithms, extracts task comparison trajectories and feature data from the optimization solution, and uses ANNs to learn the task priority comparison model. In the online phase, a closed-loop decision scheduling mode is constructed based on this model to achieve rapid response and accurate decision-making in dynamic uncertain production environments. Data experiments and practical application cases verify the effectiveness of this method. With the increase of the number of optical-mechanical modules, the advantages of ANN scheduling algorithm become more obvious. When the optimization results of ANN scheduling algorithm and GA algorithm are less than 6%, the computational efficiency of the former is more than 400 times that of the latter.
- artificial neural network,
- scheduling rules,
- intelligent assembly scheduling

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References(10)

References

[1]	郑万国, 邓颖, 周维, 等. 激光聚变研究中心激光技术研究进展[J]. 强激光与粒子束, 2013, 25(12):3082-3090 doi: 10.3788/HPLPB20132512.3082 Zheng Wanguo, Deng Ying, ZhouWei, et al. Development of laser technology in research center of laser fusion[J]. High Power Laser and Particle Beams, 2013, 25(12): 3082-3090 doi: 10.3788/HPLPB20132512.3082
[2]	Panwalkar SS, Iskander W. A survey of scheduling rules[J]. Operations Research, 1977, 25(1): 45-61. doi: 10.1287/opre.25.1.45
[3]	张泽群, 唐敦兵, 金永乔, 等. 信息物联驱动下的离散车间自组织生产调度技术[J]. 机械工程学报, 2018, 54(16):34-44 doi: 10.3901/JME.2018.16.034 Zhang Zequn, Tang Dunbing, JinYongqiao, et al. Self-organizing production technology for discrete workshop scheduling driven by internet of things[J]. Journal of Mechanical Engineering, 2018, 54(16): 34-44 doi: 10.3901/JME.2018.16.034
[4]	龙田, 石宇强, 王俊佳. 柔性作业车间在线调度问题的仿真建模与分析[J]. 机械设计与制造, 2015(12):27-30 doi: 10.3969/j.issn.1001-3997.2015.12.008 Long Tian, Shi Yuqiang, Wang Junjia. Simulation modeling and analysis for online flexible job-shop scheduling problem[J]. Machinery Design & Manufacture, 2015(12): 27-30 doi: 10.3969/j.issn.1001-3997.2015.12.008
[5]	Burke E K, Hyde M, Kendall G, et al. A survey of hyper-heuristics[R]. Nottingham: University of Nottingham, 2009.
[6]	范华丽, 熊禾根, 蒋国璋, 等. 动态车间作业调度问题中调度规则算法研究综述[J]. 计算机应用研究, 2016, 33(3):648-653 doi: 10.3969/j.issn.1001-3695.2016.03.002 Fan Huali, Xiong Hegen, Jiang Guozhang, et al. Survey of dispatching rulesfor dynamic job-shop scheduling problem[J]. Application Research of Computers, 2016, 33(3): 648-653 doi: 10.3969/j.issn.1001-3695.2016.03.002
[7]	Mouelhi-Chibani W, Pierreval H. Training a neural network to select dispatching rules in real time[J]. Computers & Industrial Engineering, 2010, 58(2): 249-256.
[8]	Golmohammadi D. A neural network decision-making model for job-shop scheduling[J]. International Journal of Production Research, 2013, 51(17): 5142-5157. doi: 10.1080/00207543.2013.793476
[9]	Zhang Liping, Hu Yifan, Wang Chuangjian, et al. Effective dispatching rules mining based on near-optimal schedules in intelligent job shop environment[J]. Journal of Manufacturing Systems, 2022, 63: 424-438. doi: 10.1016/j.jmsy.2022.04.019
[10]	Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence[M]. Cambridge: The MIT Press, 1992.

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