development and optimization of maintenance using the monte carlo method

Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. however, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. this study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options.the study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. it introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.mathematical models based on the homogeneous gamma degradation process and probability theory are employed to quantify these strategies. comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.the study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. by leveraging the monte carlo method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.