Cover Image

Graph Neural Networks for Job Shop Scheduling Problems: A Survey

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Smit, Igor G., Zhou, Jianan, Reijnen, Robbert, Wu, Yaoxin, Chen, Jian, Zhang, Cong, Bukhsh, Zaharah, Zhang, Yingqian, Nuijten, Wim
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
by: Reijnen, Robbert, et al.
Published: (2023)

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
by: Reijnen, Robbert, et al.
Published: (2025)

Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
by: Smit, Igor G., et al.
Published: (2024)

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
by: van Remmerden, Jesse, et al.
Published: (2024)

Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
by: Reijnen, Robbert, et al.
Published: (2022)

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data
by: van Remmerden, Jesse, et al.
Published: (2025)

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
by: Zhang, Cong, et al.
Published: (2024)

Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
by: Li, Minshuo, et al.
Published: (2026)

Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
by: Smit, Igor G., et al.
Published: (2024)

Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
by: Zhang, Cong, et al.
Published: (2022)

Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
by: Cao, Zhi, et al.
Published: (2026)

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling
by: Mencaroni, Andrea, et al.
Published: (2026)

Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
by: Fernández, Pablo Ariño
Published: (2025)

Self-Labeling the Job Shop Scheduling Problem
by: Corsini, Andrea, et al.
Published: (2024)

Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
by: van Remmerden, Jesse, et al.
Published: (2024)

Unsupervised anomaly detection algorithms on real-world data: how many do we need?
by: Bouman, Roel, et al.
Published: (2023)

Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
by: de Puiseau, Constantin Waubert, et al.
Published: (2024)

HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
by: Soykan, Bulent
Published: (2026)

Collaboration! Towards Robust Neural Methods for Routing Problems
by: Zhou, Jianan, et al.
Published: (2024)

Differentiable Quantum Architecture Search For Job Shop Scheduling Problem
by: Sun, Yize, et al.
Published: (2024)

Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
by: Jiang, Xia, et al.
Published: (2026)

A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes
by: Middelhuis, Jeroen, et al.
Published: (2025)

Self-Evaluation for Job-Shop Scheduling
by: Echeverria, Imanol, et al.
Published: (2025)

Variational Approach for Job Shop Scheduling
by: Oh, Seung Heon, et al.
Published: (2026)

A Non-Variational Quantum Approach to the Job Shop Scheduling Problem
by: Lopez-Ruiz, Miguel Angel, et al.
Published: (2025)

Learning-Based Approaches for Job Shop Scheduling Problems: A Review
by: Rihane, Karima, et al.
Published: (2025)

Cross-Problem Learning for Solving Vehicle Routing Problems
by: Lin, Zhuoyi, et al.
Published: (2024)

RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
by: Xiao, Xiangjie, et al.
Published: (2026)

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
by: Liu, Wei, et al.
Published: (2026)

Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing
by: Abouelrous, Abdo, et al.
Published: (2025)

Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
by: Zhang, Rui, et al.
Published: (2026)

Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
by: Lassoued, Sofiene, et al.
Published: (2026)

Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem
by: Boveroux, Laurie, et al.
Published: (2025)

Updating Lower and Upper Bounds for the Job-Shop Scheduling Problem Test Instances
by: Graviers, Marc-Emmanuel Coupvent des, et al.
Published: (2025)

Complexity Indicators applied to the Job Shop Scheduling Problem to discriminate the best Algorithm
by: Jorge A. Ruiz-Vanoye
Published: (2011)

Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
by: Bi, Jieyi, et al.
Published: (2026)

Instance Configuration for Sustainable Job Shop Scheduling
by: Perez, Christian, et al.
Published: (2024)

End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
by: Abouelrous, Abdo, et al.
Published: (2025)

Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
by: Jiang, Xia, et al.
Published: (2024)

Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application
by: Abouelrous, Abdo, et al.
Published: (2025)