Monte Carlo simulation for evaluating and optimizing the efficiency of BR and QIR maintenance strategies

This study provides a comprehensive evaluation of maintenance strategies for production systems subjected to stochastic degradation, with a comparative analysis of both traditional and advanced approaches. Traditional models, such as block replacement (BR) and quantile-based inspection and replacement (QIR), are critically examined in terms of their cost-effectiveness and robustness. While BR relies on fixed replacement intervals, QIR dynamically adjusts inspection schedules based on system degradation profiles, aiming to optimize maintenance expenditures and enhance system availability. However, as industrial systems grow increasingly complex, there is a clear need for more advanced maintenance strategies that offer superior performance and adaptability. In this context, the study explores advanced strategies, including the proportional hazards model (PHM) for condition-based maintenance (CBM), reinforcement learning-based maintenance (RL-M), and hybrid predictive-preventive maintenance (PPM). PHM-CBM leverages real-time degradation data for dynamic and optimal scheduling, RL-M utilizes machine learning algorithms to iteratively refine maintenance decisions, and Hybrid PPM integrates predictive analytics with preventive actions to ensure consistent cost control and system reliability. To facilitate a rigorous evaluation, this study proposes a novel cost criterion that integrates long-term cost rate projections with the variability observed across renewal cycles, providing a balanced assessment of both performance and robustness. The analysis is conducted using Monte Carlo simulations and stochastic renewal theory, offering a benchmark for comparing the various maintenance strategies. Ultimately, the study underscores the importance of further quantitative comparisons between advanced and traditional maintenance policies.