arXiv cs.AI· Ziheng Zhang, Dhivya Dharshini Kannan, Wei Zhang·· 15 小时前AI 评分20
SLAB：面向 PPVC 模块工厂灵活作业车间调度的时间滞后感知深度强化学习
SLAB: Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
AI 导读
SLAB 是一种时间滞后感知的深度强化学习调度决策过程，只在模块 12 至 48 小时的等待结束后才释放下一道工序，并以包含等待时间的可证下界下降作为奖励。在三个现有网络上，修复后的计划比同一网络在 SLAB 内的调度长 42% 至 133%；在按新加坡 PPVC 指南生成的订单上，训练出的策略在 10 至 80 个模块时比最短加工时间规则短 2.2% 至 8.1%。
正文 · 原文
Abstract:Prefabricated prefinished volumetric construction (PPVC) builds room-sized modules in factories that operate as flexible job shops. A module waits 12 to 48 h after casting, waterproofing, and painting, while its station is free for other modules. Existing learned dispatchers release the next operation before this wait ends, so their plans need repair. This paper presents SLAB (Scheduling under time-Lags with an Admissible Bound), a decision process for dispatchers trained by deep reinforcement learning. Its transition releases a module only after its wait, and its reward is the decrease of a proven lower bound that includes the waits. Training on 10-module orders with halved waits also teaches the policy the station contention of larger orders. On three existing networks, repaired plans are 42% to 133% longer than the same network's schedules inside SLAB. On orders generated from Singapore's PPVC guidebook, one trained policy gives schedules 2.2% to 8.1% shorter than the shortest-processing-time rule at 10 to 80 modules. At 50 and 80 modules, it outperforms a time-lag memetic algorithm by 6.7% and 7.4% and constraint-programming search started from that rule's schedule and given 600 s by 6.6% and 7.6%. The memetic algorithm gives shorter schedules on orders of 10 modules, and the same search given 300 s on orders of 10 and 20 modules. A module factory can therefore train one dispatcher on small orders and run it without a solver on production batches of 50 to 80 modules.
| Comments: | 12 pages, 4 figures, with a 31-page supplementary material appended. Submitted to IEEE Transactions on Industrial Informatics |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) |
| Cite as: | arXiv:2607.11725 [cs.LG] |
| (or arXiv:2607.11725v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.11725 arXiv-issued DOI via DataCite |
Submission history
From: Ziheng Zhang [view email]
[v1]
Mon, 13 Jul 2026 15:50:00 UTC (184 KB)
[v2]
Sat, 24 Oct 2026 08:40:07 UTC (593 KB)
来源：arXiv cs.AI · arxiv.org