Optimal maintenance scheduling under uncertainties using Linear Programming-enhanced Reinforcement Learning

Maintenance is of great importance for the safety and integrity of infrastructures. The expected optimal maintenance policy in this study should be able to minimize system maintenance cost while satisfying the system reliability requirements. Stochastic maintenance scheduling with an infinite horizon has not been investigated thoroughly in the literature. In this work, we formulate the maintenance optimization under uncertainties as a Markov Decision Process (MDP) problem and solve it using a modified Reinforcement Learning method. A Linear Programming-enhanced RollouT (LPRT) is proposed, which considers both constrained deterministic and stochastic maintenance scheduling with an infinite horizon. The novelty of the proposed approach is that it is suitable for online maintenance scheduling, which can include random unexpected maintenance performance and system degradation. The proposed method is demonstrated with numerical examples and compared with several existing methods. Results show that LPRT is able to determine the suitable optimal maintenance policy efficiently compared with existing methods with similar accuracy. Parametric studies are used to investigate the effect of uncertainty, subproblem size, and the number of stochastic stages on the final maintenance cost. Limitations and future work are given based on the proposed study.

Automated summary

Maintenance is crucial for the safety and integrity of infrastructures. This study aims to find the optimal maintenance policy that minimizes the maintenance cost while meeting system reliability requirements. By formulating the maintenance optimization as a Markov Decision Process and using a modified Reinforcement Learning method, the proposed approach, LPRT, considers both deterministic and stochastic maintenance scheduling with an infinite horizon. Numerical examples and comparisons with existing methods demonstrate the effectiveness and accuracy of LPRT. Parametric studies provide insights into the impact of uncertainty, subproblem size, and the number of stochastic stages on the maintenance cost.