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English(EN) A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory

深度强化学习利用RFID数据优化智能工厂调度

本文介绍了一种新颖的动态车间生产调度框架,适用于利用RFID技术的智能工厂。该方法通过分析RFID收集的数据来估计生产不确定性并挖掘可行的生产序列,从而应对制造过程中的不确定性。采用了一种深度强化学习方法,特别是深度Q网络(DQN),来优化调度,在缩短完工时间方面优于FIFO和LIFO等传统方法。 AI

影响 这项研究通过改进智能工厂的生产调度,有望提高制造运营效率。

排序理由 该条目是一篇学术论文,详细介绍了一种新的生产调度方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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深度强化学习利用RFID数据优化智能工厂调度

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该条目是一篇学术论文,详细介绍了一种新的生产调度方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhihui Chen, Yize Sun, Yuhao Dong, Zeyu Xiao, Ray Y. Zhong ·

    基于RFID支持的智能工厂的车间生产调度案例

    arXiv:2608.16626v1 Announce Type: new Abstract: Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. W…