PhD Symposium Stochastic_Optimisation_of_Tail_Assignment_and_Maintenance_Task_Scheduling_with_health_aware_models

Abstract

Efficient maintenance management is essential—not only to reduce costs but also to maximize aircraft availability and uphold safety standards. This requires balancing maintenance scheduling (MS), which drives downtime, with tail assignment (TA), which governs aircraft utilization. While recent research has explored the integration of MS and TA, these efforts have largely neglected the role of Condition-Based Maintenance (CBM) and the uncertainty inherent in prognostic models. This research proposes a novel, unified framework that jointly optimizes MS, TA, and CBM using stochastic programming and health-aware models. The approach leverages sensor-derived prognostic information to forecast component degradation and incorporates its probabilistic nature directly into the planning process. By accounting for uncertainty in remaining useful life (RUL) predictions, the model produces robust flight and maintenance schedules that reduce the risk of unplanned disruptions. Preliminary experiments using real-world airline data demonstrate that explicitly modeling health uncertainty leads to more reliable scheduling outcomes, while improving operational efficiency and reducing maintenance costs. Compared to current industry practice, the integrated framework enables data-driven, future-oriented decision-making at the interface between fleet operations and maintenance planning. This work advances the state-of-the-art by holistically addressing TA, MS, and CBM within a scalable and interpretable optimization model—closing a critical gap in the practical deployment of CBM strategies in civil aviation.