Transportation Research Part B 133 (2020) 1–23 Contents lists available at ScienceDirect Transportation Research Part B journal homepage: www.elsevier.com/locate/trb An optimisation framework for airline fleet maintenance scheduling with tail assignment considerations David Torres Sanchez a , Burak Boyacı b , Konstantinos G. Zografos b,∗ a STOR-i CDT, Lancaster University, Lancaster, LA1 4YX, UK b Centre for Transport and Logistics (CENTRAL), Lancaster University Management School, Lancaster, LA1 4YX, UK a r t i c l e i n f o Article history: Received 3 March 2019 Revised 14 November 2019 Accepted 22 December 2019 Keywords: Airline maintenance scheduling Tail assignment Multi-objective mixed integer linear programming a b s t r a c t 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 ded- icated to aircraft maintenance, cooperation between airlines and their respective main- tenance 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) for- mulations and an iterative algorithm. The first formulation, the airline fleet maintenance scheduling (AMS) with violations, minimises the number of maintenance regulation viola- tions and the number of not airworthy aircraft; subject to limited workshop resources and current maintenance regulations on individual aircraft flying hours. The second formula- tion, 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, over- all 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 fly- ing 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 hori- zon 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. © 2020 Elsevier Ltd. All rights reserved. 1. Introduction There are a number of operational decisions associated with airlines, from ticket prices to flight times, crew rosters, and aircraft maintenance. When making these decisions, airlines have to take into account their own economic interests influenced by demand, costs, and sometimes even the actions of their competitors. In such a competitive environment, airlines aim to minimise their operating costs while providing competitive services. Significant proportion of operating costs are dedicated to maintenance. For instance, 20.5% of the average direct operating cost per medium-haul trip are dedicated ∗ Corresponding author. E-mail addresses: d.torressanchez@lancaster.ac.uk (D.T. Sanchez), b.boyaci@lancaster.ac.uk (B. Boyacı), k.zografos@lancaster.ac.uk (K.G. Zografos). https://doi.org/10.1016/j.trb.2019.12.008 0191-2615/© 2020 Elsevier Ltd. All rights reserved.