An Optimisation Framework for Airline Fleet Maintenance Scheduling with Tail Assignment Considerations

Torres Sanchez, David and Boyacı, Burak and Zografos, K. G. (2020) An Optimisation Framework for Airline Fleet Maintenance Scheduling with Tail Assignment Considerations. Transportation Research Part B: Methodological, 133. pp. 142-164. ISSN 0191-2615

Text (sanchez2020postprint)
torressanchez2020postprint.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (419kB)

Text (sanchez2020preprint)
sanchez2020preprint.pdf - Submitted Version
Download (487kB)

Abstract

Fierce competition between airlines has led to the need of minimising the operating costs while also ensuring quality of service. Given the large proportion of operating costs dedicated to aircraft maintenance, cooperation between airlines and their respective maintenance provider is paramount. In this research, we propose a framework to develop commercially viable and maintenance feasible flight and maintenance schedules. Such framework involves two multi-objective mixed integer linear programming (MMILP) formulations and an iterative algorithm. The first formulation, the airline fleet maintenance scheduling (AMS) with violations, minimises the number of maintenance regulation violations and the number of not airworthy aircraft; subject to limited workshop resources and current maintenance regulations on individual aircraft flying hours. The second formulation, the AMS with tail assignment (TA) allows aircraft to be assigned to different flights. In this case, subject to similar constraints as the first formulation, six lexicographically ordered objective functions are minimised. Namely, the number of violations, maximum resource level, number of tail reassignments, number of maintenance interventions, overall resource usage, and the amount of maintenance required by each aircraft at the end of the planning horizon. The iterative algorithm ensures fast computational times while providing good quality solutions. Additionally, by tracking aircraft and using precise flying hours between maintenance opportunities, we ensure that the aircraft are airworthy at all times. Computational tests on real flight schedules over a 30-day planning horizon show that even with multiple airlines and workshops (16000 flights, 529 aircraft, 8 maintenance workshops) our solution approach can construct near-optimal maintenance schedules within minutes.