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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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

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

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