用于 PPVC 模块工厂中灵活作业车间调度的时间滞后感知深度强化学习
Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
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中文总结 AI 辅助
研究 PPVC 模块工厂灵活作业车间调度问题，通过对先进深度强化学习求解器进行三个扩展，得到时间滞后感知的学习策略，该策略在基准实例上表现出色，是无求解器调度器中最强的，还能快速重新规划。
中文摘要 AI 辅助
预制装配式建筑将大部分建筑工作转移到模块工厂，其生产车间作为一个灵活的作业车间运行。一个主要的复杂因素是决定性的：混凝土养护、防水蓄水试验和油漆干燥会导致较长的操作后时间滞后，在此期间模块被阻塞而其工作站空闲。在基于官方国家预制指南的基准实例上，这些滞后平均使最优参考完工时间膨胀约 67%，并且在决策时忽略它们然后修复到可行性比任何调度规则都差。我们通过三个微创、可单独消融的扩展来调整一个先进的双注意力深度强化学习求解器：具有可接受奖励界限的滞后感知动力学、两个预期滞后特征通道以及活跃度掩码操作和站型嵌入。禁用每个扩展时，实现会精确再现原始求解器，因此所有收益都归因于这些调整。我们发布了一个基于指南的公共基准生成器。在留出的实例上，学习到的策略是最强的无求解器调度器：它达到约束编程参考的约 4%以内，击败了每个调度规则和一个遗传算法元启发式算法，在容量竞争下其优势扩大，并且单一的尺寸混合策略在工厂规模的训练范围内保持领先。它在循环中不需要求解器、模型或许可证，并且在中断后几秒钟内重新规划；在可以部署精确求解器的地方，该求解器仍然是质量上限，我们明确绘制了这个边界。
英文摘要
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.
发表机构
- Singapore Institute of Technology（新加坡科技学院）
机构由 AI 辅助整理，请以论文原文为准。
