Aircraft Maintenance Planning
Genetic Algorithm Optimization of Aircraft Hangar Maintenance Planning under Uncertainty
T.J.A. Hollander (TU Delft - Aerospace Engineering)
M.J. Ribeiro – Mentor (TU Delft - Operations & Environment)
M.M.D. Witteman – Mentor (TUI Airlines Nederland B.V.)
M.F.M. Hoogreef – Graduation committee member (TU Delft - Flight Performance and Propulsion)
M. Popovic – Graduation committee member (TU Delft - Control & Simulation)
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Abstract
Aircraft maintenance planning plays a large role in ensuring operational efficiency and safety while minimising costs. Hangar maintenance scheduling can be trivial due to various uncertainties, such as non-routine tasks, resource availability, and unforeseen delays. Deterministic methods might struggle to account for these complexities and do not scale well with large, heterogeneous fleets, causing frequent and costly adjustments to the schedule. Previous research has focused on incorporating different uncertainties using robust scheduling methods. This research aims to develop and assess a stochastic and scalable aircraft hangar maintenance planning model that can provide insight in the robustness of the planning, next to incorporated uncertainties, to reduce the need for frequent planning revisions. The proposed method creates a schedule using a Genetic Algorithm (GA) that minimises maintenance costs and interval losses while adhering to operational constraints. After that, a Monte Carlo simulation is applied to assess the feasibility of the schedule under randomly generated check duration scenarios. Critical checks that can cause grounding of aircraft due to exceeded due dates are modified in a feedback loop to improve the robustness of the schedule. The maintenance optimisation is tested in a case study, provided by a European airline and discusses the trade-off between maintenance cost, interval loss, run time, and feasibility in hangar maintenance planning under uncertainty. The schedule is compared to a Mixed-Integer Linear Programming (MILP) benchmark model. Results show that the MILP outperforms the MILP in terms of cost and run time, but the GA might be useful in more complex scenarios. The simulations give insight in the robustness of the planning and show that delay propagation and grounding probabilities can be decreased by adjusting critical checks during re-optimisation. The overall grounding probability can go down by 9 to 40%, with 5 to 10% of checks fixed in time, respectively. This can lead to a more robust schedule, minimising revisions. An airline can use this framework as a decision-support tool to create variations on the planning and assess the impact of its decisions on the robustness of the schedule.