The MRO market currently spans around 9.5% of the total operating cost of an airline. Of this, 70% is covered by heavy-maintenance. Reduction of these costs and improving efficiency could, therefore, be significant for an airline. A possible solution is the optimization of the long-term schedule of heavy-maintenance checks. Current approaches are found to be reliant on manual input and operator experience. Next to that, revisions to the initial schedule are made continuously due to the inherently stochastic nature of aircraft maintenance through non-routine maintenance. Taking this uncertainty into account could offer more robust schedules, saving cost and improve quality of service.

This study proposes a genetic algorithm that can generate robust and efficient C-check schedules for a fleet of heterogeneous aircraft. Uncertainty in check duration and utilization are taken into account by assessing multiple scenarios through min-max optimization. This study is the first to address the long-term scheduling of heavy-maintenance checks while taking uncertainty into account. The proposed genetic algorithm finds robust and efficient C-check schedules for a case study of a European airline for a fleet of over 40 aircraft in under 30 minutes. The total number of C-checks is reduced by 7% while increasing utilization by 4.4%. This could lead to a reduction of direct annual maintenance costs of $122.5K - $612.5K and an additional $1.8M - $7.1M in annual revenue due to the increased availability of aircraft. Monte Carlo simulations show that with a probability of 41% no adjustments to the schedule are necessary over the planning horizon.