用离线强化学习优化车间调度，兼顾专家经验与目标优化。
Offline reinforcement learning for job-shop scheduling problems
- 将状态建模为异构图，动作编码在边属性中，适配复杂约束。
- 在作业车间和柔性车间调度任务上超越现有最优方法。
- 适合需要高效求解复杂调度问题的研究者或工业应用者。
深度学习在实时组合优化问题求解方面展现出巨大潜力。与传统方法不同，深度学习能高效生成高质量解，对路径规划和调度等应用至关重要。然而，现有的深度强化学习（RL）和行为克隆方法存在明显局限：深度强化学习学习速度慢，而行为克隆仅依赖专家动作，易导致泛化能力差并忽略优化目标。本文提出一种针对具有复杂约束的组合优化问题的新型离线强化学习方法，将状态表示为异构图，动作空间可变，通过边属性编码动作，并平衡期望奖励与专家解的模仿。在作业车间调度和柔性作业车间调度基准测试中，该方法表现优于当前最优技术。
原文摘要 · Abstract (English)
Recent advances in deep learning have shown significant potential for solving combinatorial optimization problems in real-time. Unlike traditional methods, deep learning can generate high-quality solutions efficiently, which is crucial for applications like routing and scheduling. However, existing approaches like deep reinforcement learning (RL) and behavioral cloning have notable limitations, with deep RL suffering from slow learning and behavioral cloning relying solely on expert actions, which can lead to generalization issues and neglect of the optimization objective. This paper introduces a novel offline RL method designed for combinatorial optimization problems with complex constraints, where the state is represented as a heterogeneous graph and the action space is variable. Our approach encodes actions in edge attributes and balances expected rewards with the imitation of expert solutions. We demonstrate the effectiveness of this method on job-shop scheduling and flexible job-shop scheduling benchmarks, achieving superior performance compared to state-of-the-art techniques.
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