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Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

arXiv 2010.12367 ↗First posted 23 Oct 2020cs.LG cs.AI stat.MLcited by 139

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Abstract (arXiv)

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 13; 2023: 30; 2024: 30; 2025: 22; 2026: 32.

139 papers.

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  1. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  2. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  3. 2021Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformercited by 108
  4. 2021Efficient Active Search for Combinatorial Optimization Problemscited by 99
  5. 2022Simulation-guided Beam Search for Neural Combinatorial Optimizationcited by 84
  6. 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimizationcited by 80
  7. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 70
  8. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 68
  9. 2023Robust Scheduling with GFlowNetscited by 67
  10. 2023Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policycited by 61
  11. 2022Winner Takes It All: Training Performant RL Populations for Combinatorial Optimizationcited by 57
  12. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
38 more
  1. 2023Combinatorial Optimization with Policy Adaptation using Latent Space Searchcited by 41
  2. 2024GOAL: A Generalist Combinatorial Optimization Agent Learnercited by 39
  3. 2025Rethinking Light Decoder-based Solvers for Vehicle Routing Problemscited by 36
  4. 2021Learning Large Neighborhood Search Policy for Integer Programmingcited by 35
  5. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 32
  6. 2021ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningcited by 31
  7. 2025Large Language Models as End-to-end Combinatorial Optimization Solverscited by 27
  8. 2021A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphscited by 26
  9. 2024Self-Labeling the Job Shop Scheduling Problemcited by 23
  10. 2024Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectivescited by 23
  11. 2021A Reinforcement Learning Environment For Job-Shop Schedulingcited by 21
  12. 2021Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problemscited by 20
  13. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  14. 2023Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learningcited by 20
  15. 2022Learning to Control Local Search for Combinatorial Optimizationcited by 18
  16. 2021Learning Practically Feasible Policies for Online 3D Bin Packingcited by 16
  17. 2023Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applicationscited by 16
  18. 2025REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Taskscited by 16
  19. 2024Graph Neural Networks for Job Shop Scheduling Problems: A Surveycited by 15
  20. 2025Learning to Reduce Search Space for Generalizable Neural Routing Solvercited by 14
  21. 2025BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimizationcited by 14
  22. 2023Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problemcited by 12
  23. 2025Starjob: Dataset for LLM-Driven Job Shop Schedulingcited by 12
  24. 2023An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programmingcited by 11
  25. 2023Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methodscited by 11
  26. 2024LLMs can Schedulecited by 11
  27. 2024Collaboration! Towards Robust Neural Methods for Routing Problemscited by 9
  28. 2023Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directionscited by 8
  29. 2022Learning to generalize Dispatching rules on the Job Shop Schedulingcited by 7
  30. 2023Neural Airport Ground Handlingcited by 7
  31. 2024PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimizationcited by 7
  32. 2025Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Schedulingcited by 7
  33. 2021An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentscited by 6
  34. 2023Efficient Training of Multi-task Neural Solver for Combinatorial Optimizationcited by 6
  35. 2023Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimizationcited by 6
  36. 2024Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learningcited by 6
  37. 2024Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardinesscited by 6
  38. 2024Offline Reinforcement Learning for Learning to Dispatch for Job Shop Schedulingcited by 6

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