Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning

• Published in: International journal of production research 2021-06, Vol.59 (11), p.3360-3377
• Main Authors: Park, Junyoung, Chun, Jaehyeong, Kim, Sang Hun, Kim, Youngkook, Park, Jinkyoo
• Format: Article
• Language: English
• Subjects: Decision making; Dispatching rules; graph neural network; Graph neural networks; Graph representations; Graphical representations; Job shop scheduling; job shop scheduling problem; Job shops; JSSP; Learning; Neural networks; Optimization; reinforcement learning; Scheduling
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2020.1870013

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 favoured 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.