English(EN) BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport

BOLT学习方法优化带运输耦合的车间调度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了BOLT，一种新颖的多瓶颈学习方法，用于柔性车间调度，特别解决了稀缺车队在站点间运输工件的场景。该方法构建了一个下界来指导学习过程，该下界的变动直接告知策略运输的成本。BOLT旨在通过考虑运输对总体完成时间的直接影响来优化调度，在建筑和生产批次实例上展示了改进的性能。 AI

影响这项研究为优化复杂的调度问题引入了一种新颖的基于学习的方法，有望提高制造和建筑物流的效率。

排序理由该条目是发表在arXiv上的研究论文，详细介绍了一种新的车间调度算法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ziheng Zhang, Wei Zhang, Yunyi Zhao, Zuming Liu, Wei Zhou, Boon Ping Gan · 2026-10-19 04:00

BOLT：具有时滞和资源耦合运输的灵活作业车间调度多瓶颈约束引导学习

arXiv:2610.22433v1 Announce Type: new Abstract: Flexible job-shop scheduling becomes resource-coupled when a scarce vehicle fleet moves jobs between stations, because the schedule sets the transport waits. BOLT (bound-oriented learning for transport-coupled scheduling) builds a l…