用于作业车间调度问题的离线强化学习
Offline reinforcement learning for job-shop scheduling problems
AI总结：
本文提出一种新型离线强化学习方法，通过异构图表示状态、边属性编码动作并平衡奖励与专家模仿，有效解决了具有复杂约束的作业车间调度问题，性能优于现有技术。
摘要：
深度学习的最新进展已显示出在实时解决组合优化问题方面的巨大潜力。与传统方法不同，深度学习能高效生成高质量解，这对路由和调度等应用至关重要。然而，现有的深度强化学习（RL）和行为克隆等方法存在明显局限：深度RL学习缓慢，而行为克隆仅依赖专家动作，易导致泛化问题且忽略优化目标。本文提出一种针对复杂约束组合优化问题的新型离线RL方法，其状态表示为异构图，动作空间可变。该方法将动作编码于边属性中，并平衡期望奖励与专家解的模仿。我们在作业车间调度和柔性作业车间调度基准上验证了该方法的有效性，相比最先进技术取得了更优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
