mlsanityData to 14 Oct 2026

Offline reinforcement learning for job-shop scheduling problems

arXiv 2410.15714 ↗First posted 21 Oct 2024cs.LG cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

Recent advances in deep learning have shown significant potential for solving combinatorial optimization problems in real-time. Unlike traditional methods, deep learning can generate high-quality solutions efficiently, which is crucial for applications like routing and scheduling. However, existing approaches like deep reinforcement learning (RL) and behavioral cloning have notable limitations, with deep RL suffering from slow learning and behavioral cloning relying solely on expert actions, which can lead to generalization issues and neglect of the optimization objective. This paper introduces a novel offline RL method designed for combinatorial optimization problems with complex constraints, where the state is represented as a heterogeneous graph and the action space is variable. Our approach encodes actions in edge attributes and balances expected rewards with the imitation of expert solutions. We demonstrate the effectiveness of this method on job-shop scheduling and flexible job-shop scheduling benchmarks, achieving superior performance compared to state-of-the-art techniques.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 34 of its 59 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

34 of 59 references.

Closest ancestors · 8

Same direction, newest first

  1. 2022Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning31 Oct
  2. 2022When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?12 Apr
  3. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems2 Mar
  4. 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun
  5. 2021Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism22 Mar
  6. 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun
  7. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems4 May
  8. 2020Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning19 Feb

Other references · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  6. 2015Pointer Networkscited by 895
  7. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  8. 2018Attention, Learn to Solve Routing Problems!cited by 656
  9. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  10. 2020Heterogeneous Graph Transformercited by 582
  11. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  12. 2021Implicit Behavioral Cloningcited by 452
14 more
  1. 2015Policy Distillationcited by 440
  2. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 348
  3. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  4. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  5. 2020Human-centric Dialog Training via Offline Reinforcement Learningcited by 93
  6. 2021ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningcited by 31
  7. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 30
  8. 2024Self-Labeling the Job Shop Scheduling Problemcited by 23
  9. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  10. 2023Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learningcited by 20
  11. 2022Learning to Control Local Search for Combinatorial Optimizationcited by 18
  12. 2023Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problemcited by 12
  13. 2023An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programmingcited by 11
  14. 2024Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problemcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Variational Approach for Job Shop Schedulingcited by 0
  2. 2026PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problemcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
  2. 2024Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
  3. 2024Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
  4. 2024Graph Neural Networks for Job Shop Scheduling Problems: A Survey
  5. 2026Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem
  6. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
  7. 2024Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
  8. 2026HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers