Data-driven based aircraft maintenance routing by markov decision process model

Aircraft maintenance routing is of basic significance to the safe and efficient operations of an airline. However, the timely efficiency of the airline flight schedule is susceptible to various factors during the daily operations. Air traffic often undergoes some random disruptions that expose maintenance routing to random flight delays, which have to be considered to ensure safe and operational flight schedule. The idea of data-driven methods was the focal point of much studies during a previous couple of years. Constrained Markov Decision process model was selected in this paper to remedy this problem and design the maintenance needs of an aircraft taking past data information into account. Maintenance actions are so modeled with stochastic state transitions. This can offer the opportunity to solve the maintenance routing problem deliberating and handling flight disturbances. Through computational tests on real data of a Moroccan airline company, we investigate the efficiency of this solution approach on history data sets.