An optimisation framework for airline fleet maintenance scheduling with tail assignment considerations

• Published in: Transportation research. Part B: methodological 2020-03, Vol.133, p.142-164
• Main Authors: Sanchez, David Torres, Boyacı, Burak, Zografos, Konstantinos G.
• Format: Article
• Language: English
• Subjects: Aircraft; Aircraft maintenance; Aircraft reliability; Airline maintenance scheduling; Airline operations; Airlines; Algorithms; Commercial aircraft; Computer applications; Flight; Integer programming; Iterative algorithms; Iterative methods; Linear programming; Maintenance management; Mixed integer; Multi-objective mixed integer linear programming; Multiple objective analysis; Operating costs; Optimization; Schedules; Scheduling; Tail assignment; Tails; Violations; Workshops
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2019.12.008

•Developments of two new Multi-Objective Mixed Integer Linear Programming (MMILP) formulations for the Airline Fleet Maintenance Scheduling problem.•Development of an iterative algorithm for solving large-scale real world instances efficiently.•Testing of the solution methodology using a 30-day planning horizon with multiple airlines, with cases having 5-8 workshops, 6871-16000 flights, and 216-529 aircraft respectively. 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.