Efficient maintenance management requires an integrated approach that balances downtime (maintenance scheduling, MS) and uptime (tail assignment, TA). Current methods often use sequential decision-making, which neglects the interdependencies between MS and TA, resulting in sub optimal outcomes. Recent research has started to integrate MS and TA modelling, and separately also to integrate MS with predictive maintenance (PM) strategies. These trends highlight the need for a comprehensive approach that optimises these interdependencies and considers the uncertainties of prognostic models.

This research presents a new scheduling framework for optimal maintenance management that integrates PM, MS and TA. Historical sensor data is used with Gaussian Process Regression to predict the end-of-life of aircraft components and quantify prediction uncertainties. These uncertainties are incorporated into the decision-making process using Monte Carlo simulations. The resulting model assigns aircraft and tasks to maintenance slots and flight legs under various predictor scenarios, enabling a more adaptive scheduling strategy.

A case study with Swiss International Air Lines over a three-day period for five aircraft demonstrates the model's effectiveness, showing improvements in operational efficiency, reduced costs, fewer cancellations, and higher fleet availability. The model's predictions and schedules were validated by expert planners, suggesting that a holistic, adaptive scheduling approach has significant potential for operational improvements in the airline industry.