Integrated Tail Assignment and Maintenance Task Scheduling

A Decision Support Framework for Airline Schedule Efficiency and Stability

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This study presents a new approach for integrated tail assignment and maintenance task scheduling decision support. Wasted maintenance resources, restricted fleet availability for schedule flexibility, inconsistent planning, and no emphasis on schedule resilience are all inefficiencies that now arise from airlines' separate and manual tail assignment and maintenance task scheduling. The framework addresses these issues, something not yet discussed in the literature. The framework's objective is to deliver feasible plans that increase schedule efficiency (no cancellations, high fleet availability, high fleet health, and optimal use of maintenance resources) and schedule stability (limited number of late arrival disruptions during operations) the day before operation. At its core, a mixed integer linear programming optimization model abides by airline-specific requirements and priorities. The schedule is modeled using an innovative two-space time-space network (TSN), with one space dedicated to maintenance and the other network activities. Stability is introduced with additional ground arcs positioned after flights that, if assigned, allow the creation of flight-specific buffers based on flights' historical arrival delay distributions. Task scheduling is done in registration specific slots, based on aircraft's individual maintenance needs, to improve schedule efficiency. The performance of the framework is tested using a case study provided by a major European single hub-to-spoke airline, with a heterogeneous fleet of over 50 wide-body aircraft. Comparison against the airline's plans over seven days shows that the proposed approach can provide real-time decision assistance and produce more efficient and stable plans. A 17% reduction in maintenance time was achieved, consequently resulting in a 10% increase in fleet availability on the day of operations. This was possible through higher labor and task interval utilization, suggesting that the framework is more efficient at scheduling maintenance tasks. Lastly, the framework's plans were more resilient to arrival delays, reducing the number of disruptions and propagated delay on the day of operations by over 40%.