这篇论文给出了一个能自动处理混凝土养护延迟的调度方法，比遗传算法和调度规则都强，接近最优解的4%，还开源了测试基准，搞工厂排程的值得看看。
该论文针对预制装配式建筑模块工厂中的柔性作业车间调度问题，提出一种时间延迟感知的深度强化学习求解器。混凝土养护、闭水试验和油漆干燥等工序造成的长时间滞后使最优参考工期平均增加约67%。方法在原始双注意力DRL基础上引入三项扩展：延迟感知动态与可接受奖励边界、两个预测延迟特征通道、以及活跃性屏蔽的操作与工位类型嵌入。在基准实例上，学习到的调度策略比所有调度规则和遗传算法元启发式更强，与约束规划参考的差距约4%，且优势在产能紧张时扩大。无需求解器可在数秒内重新规划，并开源了基于国家指南的基准生成器。
Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop. A major complication is decisive: long post-operation time-lags caused by concrete curing, watertightness ponding tests, and paint drying, during which a module is blocked while its workstation stays free. On benchmark instances grounded in an official national prefabrication guidebook, these lags inflate even the optimal reference makespan by about 67% on average, and ignoring them at decision time, then repairing to feasibility, is worse than every dispatching rule. We adapt a state-of-the-art dual-attention deep reinforcement learning solver through three minimally invasive, individually ablatable extensions: lag-aware dynamics with an admissible reward bound, two anticipatory lag feature channels, and liveness-masked operation- and station-type embeddings. With every extension disabled the implementation reproduces the original solver exactly, so all gains are attributable to the adaptations. We release a public, guidebook-grounded benchmark generator. On held-out instances the learned policy is the strongest solver-free scheduler: it reaches within about 4% of a constraint-programming reference and beats every dispatching rule and a genetic-algorithm metaheuristic, with its advantage widening under capacity contention, and a single size-mixed policy carries this lead across the trained range of factory sizes. It needs no solver, model, or license in the loop and re-plans within seconds of a disruption; where an exact solver can be deployed, that solver remains the quality ceiling, a boundary we map explicitly.