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English(EN) Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information

新的贝叶斯方法优化不确定性下的间歇式生产过程调度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的贝叶斯动态调度方法，以应对多用途间歇式生产过程中的挑战。这种新方法利用贝叶斯网络学习干扰的概率分布，例如在动态制造环境中常见的处理延迟和需求变化。通过在观察到新的干扰时更新后验分布，该方法比传统的周期性重新调度更能有效地指导重新调度策略。对基准问题的比较分析表明，贝叶斯动态调度方法在降低长期成本和减少系统波动方面取得了优越的性能。 AI

影响 这项研究可能为处理复杂、动态生产环境的行业带来更高效、更稳定的制造流程。

排序理由 关于过程调度新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

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新的贝叶斯方法优化不确定性下的间歇式生产过程调度

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关于过程调度新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Taicheng Zheng, Dan Li, Jie Li ·

    不完全前瞻信息下多用途间歇式生产过程的贝叶斯动态调度

    arXiv:2512.01093v2 Announce Type: replace-cross Abstract: Multipurpose batch processes become increasingly popular in manufacturing industries since they adapt to low-volume, high-value products and shifting demands. These processes often operate in a dynamic environment, which f…

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