We propose a framework to learn to schedule a job-shop problem (JSSP) using a
graph neural network (GNN) and reinforcement learning (RL). We formulate the
scheduling process of JSSP as a sequential decision-making problem with graph
representation of the state to consider the structure of JSSP. In solving the
formulated problem, the proposed framework employs a GNN to learn that node
features that embed the spatial structure of the JSSP represented as a graph
(representation learning) and derive the optimum scheduling policy that maps
the embedded node features to the best scheduling action (policy learning). We
employ Proximal Policy Optimization (PPO) based RL strategy to train these two
modules in an end-to-end fashion. We empirically demonstrate that the GNN
scheduler, due to its superb generalization capability, outperforms practically
favored dispatching rules and RL-based schedulers on various benchmark JSSP. We
also confirmed that the proposed framework learns a transferable scheduling
policy that can be employed to schedule a completely new JSSP (in terms of size
and parameters) without further training.

本研究提出一种基于图神经网络和强化学习的框架来解决工厂作业车间调度问题，该框架将调度问题表示成了状态图，并使用 GNN 进行表示学习和策略学习，最终使用 PPO 算法对模型进行优化。实验结果表明，该模型优于传统的调度规则和基于强化学习的调度器，并且可以学习到一种具有泛化能力的调度策略。