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

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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.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2021-06
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.59, no.11, pp.3360 - 3377

ISSN
0020-7543
DOI
10.1080/00207543.2020.1870013
URI
http://hdl.handle.net/10203/285606
Appears in Collection
IE-Journal Papers(저널논문)
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