mlsanityData to 14 Oct 2026

Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling

arXiv 2409.11933 ↗First posted 18 Sep 2024cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process. One promising approach is to train an RL agent as an improvement heuristic, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. Our approach utilizes a network architecture that includes Transformer encoding to learn the relationships between jobs. Afterwards, a probability matrix is generated from which pairs of jobs are sampled and then swapped to improve the solution. We benchmarked our approach against other heuristics using real data from our industry partner, demonstrating its superior performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 29 references that are in the map. First posted on arXiv 18 Sep 2024.

Builds on

5 of 29 references.

References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2021A Survey of Transformerscited by 401
  4. 2019Learning Improvement Heuristics for Solving Routing Problemscited by 164
  5. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140

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  3. 2024Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling
  4. 2026Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground
  5. 2025Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
  6. 2025Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
  7. 2025LineFlow: A Framework to Learn Active Control of Production Lines
  8. 2025Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions