mlsanityData to 14 Oct 2026

Graph Neural Networks for Job Shop Scheduling Problems: A Survey

arXiv 2406.14096 ↗First posted 20 Jun 2024cs.AI cs.LGcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 47 of its 132 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

47 of 132 references.

References · 47

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35 more
  1. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  2. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 318
  3. 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 269
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  7. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
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  12. 2022Simulation-guided Beam Search for Neural Combinatorial Optimizationcited by 84
  13. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 70
  14. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 69
  15. 2021Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learningcited by 52
  16. 2024Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalizationcited by 50
  17. 2023Combinatorial Optimization with Policy Adaptation using Latent Space Searchcited by 41
  18. 2021ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningcited by 31
  19. 2024Self-Labeling the Job Shop Scheduling Problemcited by 23
  20. 2021A Reinforcement Learning Environment For Job-Shop Schedulingcited by 21
  21. 2023Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learningcited by 20
  22. 2022Learning to Control Local Search for Combinatorial Optimizationcited by 18
  23. 2023Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methodscited by 12
  24. 2023Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problemcited by 12
  25. 2023An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programmingcited by 11
  26. 2020Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learningcited by 10
  27. 2022Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learningcited by 10
  28. 2023GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Cloudscited by 10
  29. 2022Learning to generalize Dispatching rules on the Job Shop Schedulingcited by 7
  30. 2024Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learningcited by 6
  31. 2024Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problemcited by 4
  32. 2022Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismcited by 3
  33. 2024Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problemcited by 3
  34. 2023Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learningcited by 1
  35. 2024Graph Q-Learning for Combinatorial Optimizationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 10.

15 papers.

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  1. 2025Rethinking Light Decoder-based Solvers for Vehicle Routing Problemscited by 37
  2. 2024Collaboration! Towards Robust Neural Methods for Routing Problemscited by 9
  3. 2024Offline Reinforcement Learning for Learning to Dispatch for Job Shop Schedulingcited by 6
  4. 2026Learning to Optimize Job Shop Scheduling Under Structural Uncertaintycited by 2
  5. 2024Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problemscited by 1
  6. 2025Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutionscited by 0
  7. 2026Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modelingcited by 0
  8. 2026Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Schedulingcited by 0
  9. 2026HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformerscited by 0
  10. 2026Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networkscited by 0
  11. 2026Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semanticscited by 0
  12. 2026Neural Certificate Pricing for Combinatorial Optimization Problemscited by 0
3 more
  1. 2026Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networkscited by 0
  2. 2026Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factoriescited by 0
  3. 2026Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Schedulingcited by 0

Similar, not cited

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  2. 2025Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
  3. 2025Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
  4. 2024Offline reinforcement learning for job-shop scheduling problems
  5. 2026RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
  6. 2024LLMs can Schedule
  7. 2024Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
  8. 2026Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals