Graph neural networks for job shop scheduling problems: A survey

Igor G. Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu (Corresponding author), Jian Chen, Cong Zhang, Zaharah Bukhsh, Yingqian Zhang, Wim P.M. Nuijten

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Abstract

Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems.

Original languageEnglish
Article number106914
Number of pages16
JournalComputers and Operations Research
Volume176
DOIs
Publication statusPublished - Apr 2025

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • Combinatorial optimization
  • Deep reinforcement learning
  • Flow-shop scheduling
  • Graph neural network
  • Job shop scheduling

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