The occurrence of unexpected aircraft maintenance tasks can produce expensive changes in an airline’s operation. When it comes to critical tasks, it might even cancel programmed flights. Despite this, the challenge of scheduling aircraft maintenance operations under uncertainty has received limited attention in the scientific literature. We study a dynamic airline maintenance scheduling problem, which daily decides the set of aircraft to maintain and the set of pending tasks to execute in each aircraft. The objective is to minimize the expected costs of expired maintenance tasks over the operating horizon. To increase flexibility and reduce costs, we integrate maintenance scheduling with tail assignment decisions. We formulate our problem as a Markov decision process and design dynamic policies based on approximate dynamic programming, including value function approximation, rolling horizon techniques, and a hybrid policy between the latter two that delivers the best results. In a case study based on LATAM airline, we show the value of dynamic optimization by testing our best policies against a simple airline decision rule and a deterministic relaxation with perfect future information. We suggest to schedule tasks requiring less resources first to increase utilization of residual maintenance capacity. Finally, we observe strong economies of scale when sharing maintenance resources between multiple airlines.
Integration between Dynamic Optimization and Scheduling of Batch Processes under Uncertainty: A Back-off Approach
