mlsanityData to 14 Oct 2026

Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems

arXiv 2108.12298 ↗First posted 27 Aug 2021cs.LGcited by 1

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Abstract (arXiv)

Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to production entities in order to prevent unplanned production downtime. Intelligent maintenance strategies are required that are able to adapt to the dynamics and different conditions of production systems. The paper introduces a deep reinforcement learning approach for condition-oriented maintenance scheduling in flow line systems. Different policies are learned, analyzed and evaluated against a benchmark scheduling heuristic based on reward modelling. The evaluation of the learned policies shows that reinforcement learning based maintenance strategies meet the requirements of the presented use case and are suitable for maintenance scheduling in the shop floor.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 27 Aug 2021.

Builds on

6 of 23 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  6. 2019Challenges of Real-World Reinforcement Learningcited by 291

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimizationcited by 8

Similar, not cited

Close in wording, no citation either way

  1. 2025A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs
  2. 2021An Offline Deep Reinforcement Learning for Maintenance Decision-Making
  3. 2026Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
  4. 2022A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
  5. 2024Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
  6. 2026Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
  7. 2024Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts
  8. 2024Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning