Adaptive scheduling of key processes in production lines based on fuzzy logic and reinforcement learning

Zhenhua Liu; Jinhua Lu; Haibo Li; Zhe Deng; Weiliang Liu

doi:10.1117/12.3049754

8 November 2024 Adaptive scheduling of key processes in production lines based on fuzzy logic and reinforcement learning

Zhenhua Liu, Jinhua Lu, Haibo Li, Zhe Deng, Weiliang Liu

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341616 (2024) https://doi.org/10.1117/12.3049754
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

To facilitate automation and intelligent scheduling of pivotal production line processes, a novel adaptive scheduling approach is introduced, leveraging fuzzy logic and reinforcement learning. It identifies key influencing factors— equipment status, raw material availability, order demands—as inputs for the fuzzy logic framework. By translating input values into fuzzy set membership degrees, it addresses production uncertainties. Reinforcement learning constructs an adaptive neural network model for scheduling, enacting actions guided by the strategy's generated instructions. Experimental outcomes demonstrate enhanced production efficiency, optimized resource utilization, cost reduction, and significant economic benefits for enterprises.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenhua Liu, Jinhua Lu, Haibo Li, Zhe Deng, and Weiliang Liu "Adaptive scheduling of key processes in production lines based on fuzzy logic and reinforcement learning", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341616 (8 November 2024); https://doi.org/10.1117/12.3049754

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