用图神经网络解决车间调度问题，可自定义训练环境。
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
- 构建模块化强化学习环境，支持灵活定制图表示与奖励函数。
- 仅用操作特征的模型即超越多个现有图模型，证明特征设计关键性。
- 提供可复用工具库，适合研究调度优化与GNN应用的开发者。
车间调度问题（Job Shop Scheduling Problem）是制造与排程中的典型NP-hard组合优化问题。传统方法依赖基于简单启发式的优先级规则，近年研究尝试用图神经网络（GNN）从数据中学习优先级分配。然而，训练此类模型需定制图结构、节点特征、动作空间和奖励函数，缺乏模块化工具导致实验耗时。本文提出JobShopLib，一个可定制的强化学习环境，支持灵活配置上述要素并快速构建新组件。我们通过模仿学习训练多个调度器，验证环境有效性；其中一模型仅使用单个操作特征即优于多个基于图的调度器，凸显特征设计的重要性。所提GNN模型在大规模问题上达到接近当前最优表现，表明该领域仍有巨大提升空间。JobShopLib为未来研究提供了必要工具。
原文摘要 · Abstract (English)
The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work has attempted to replace these with deep learning models, particularly graph neural networks (GNNs), that learn to assign priorities from data. However, training such models requires customizing numerous factors: graph representation, node features, action space, and reward functions. The lack of modular libraries for experimentation makes this research time-consuming. This work introduces JobShopLib, a modular library that allows customizing these factors and creating new components with its reinforcement learning environment. We trained several dispatchers through imitation learning to demonstrate the environment's utility. One model outperformed various graph-based dispatchers using only individual operation features, highlighting the importance of feature customization. Our GNN model achieved near state-of-the-art results on large-scale problems. These results suggest significant room for improvement in developing such models. JobShopLib provides the necessary tools for future experimentation.
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