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

TL;DR

This paper presents a deep reinforcement learning approach for condition-oriented maintenance scheduling in flow line systems, demonstrating its effectiveness over traditional heuristics in adapting to dynamic production conditions.

Contribution

It introduces a novel reinforcement learning method for maintenance scheduling that adapts to system conditions, outperforming benchmark heuristics.

Findings

Reinforcement learning policies effectively schedule maintenance tasks.

RL-based strategies outperform heuristic benchmarks.

The approach adapts well to dynamic production environments.

Abstract

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.