English(EN) DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Flexible Job Shop with LLM-Evolved Heuristic Portfolios

LLM进化的启发式组合增强作业车间调度的自适应性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 04:00

研究人员开发了DSevolve，一个旨在增强动态柔性作业车间中实时自适应调度的创新框架。该系统利用大型语言模型（LLM）来进化互补调度规则的组合，超越了单一规则优化。DSevolve将离线规则库构建与在线状态条件规则选择分开，能够快速适应订单到达或机器故障等变化条件。实验表明，DSevolve通过实现更低的平均完工时间，同时保持调度规则的速度和可解释性，优于单独的LLM进化规则、经典调度规则和其他基于学习的基线。 AI

影响这项研究可能导致在动态工业环境中更高效、更具响应性的调度系统，从而提高运营吞吐量和资源利用率。

排序理由该集群包含一篇研究论文，详细介绍了人工智能驱动调度的新的框架和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · XinLei Zhou, Jin Huang, Jie Yang, Xinyu Li, Liang Gao · 2026-07-29 04:00

DSevolve：在 LLM 演进的启发式组合上实现动态柔性作业车间的实时自适应调度

arXiv:2603.27628v2 Announce Type: replace Abstract: In dynamic flexible job shops, order arrivals, machine breakdowns, and processing-time deviations continually reshape the scheduling state and the priority trade-offs behind dispatching decisions. Dispatching rules are well suit…

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DSevolve：在 LLM 演进的启发式组合上实现动态柔性作业车间的实时自适应调度

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