Matías Villafranca, Felipe Delgado, Mathias Klapp
Abstract
Unexpected delays while executing aircraft maintenance tasks can result in costly operational disruptions for airlines, including expensive flight delays and overtime. In this study, we address uncertainty in maintenance task processing times by designing a daily and cost-effective aircraft maintenance schedule using two-stage stochastic programming. In the first stage, we determine which daily maintenance tasks to outsource and which to complete with in-house technicians. Additionally, we schedule each task, outsourced or in-house, for its respective aircraft, specifying the start time and assigned maintenance base. In-house tasks are further assigned to a technician’s work sequence. In the second stage, the start time of each task and the departure time of each flight are adjusted based on a specific realization of task processing times. We aim to minimize the expected costs incurred for outsourced maintenance tasks, overtime, and flight delays. To solve our model, we design an ad-hoc Adaptive Iterated Local Search heuristic that explores first-stage solutions via an efficient evaluation of the second-stage cost. We also present a proof of concept by testing our approach in a set of computationally simulated instances. Our proposed methodology yields 74% and 34% average cost savings compared to a deterministic approach assuming expected task processing times and to a conservative solution planning ahead with maximum possible processing times for each task, respectively. Moreover, we obtain 14% average cost savings compared to a benchmark solution, which plans maintenance tasks with an optimized time buffer parameter between consecutive task assignments. Furthermore, we study the cost impact of varying structural parameters, such as task granularity, processing time variability, workload, and cost structure.
