Research map / Reinforcement learning / Visual and representation RL
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the learned GNN dispatching policy instead of MOPNR | JSSP dispatching · Generated JSSP 30×20 | makespan: 2809.62 -> 2508.27 | Helped |
| Apply the policy trained on 30×20 instances instead of MOPNR | JSSP dispatching · Generated JSSP 100×20 | makespan: 6385.32 -> 6088.68 | Helped |
| Use the learned GNN dispatching policy instead of MOPNR | JSSP dispatching · Taillard 30×20 | makespan: 2888.1 -> 2603.9 | Helped |
| Use the learned GNN dispatching policy instead of MOPNR | JSSP dispatching · DMU 30×20 | makespan: 6491.9 -> 5967.4 | Helped |
Closest papers by meaning
- Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling (ICLR 2024 · similarity 0.91)
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- Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling (NeurIPS 2025 · similarity 0.88)
- Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems (AAAI 2025 · similarity 0.85)
- Training High-Level Schedulers with Execution-Feedback Reinforcement Learning for Long-Horizon GUI Automation (CVPR 2026 · similarity 0.84)
- One Step is Enough: Multi-Agent Reinforcement Learning Based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms (NeurIPS 2026 · similarity 0.84)
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