Stochastic tail assignment under recovery

The tail assignment problem (TAP) is an pivotal part of the airline planning process with the goal of enabling efficient and safe operations. As the airline industry faces in increasing number of schedule disruptions, taking into account uncertainty during scheduling receives increasing interest by researchers. This paper presents a novel stochastic model for TAP in order to provide robust flight schedules despite operational perturbations, as induced by, e.g., flight delay and airport closure. The model is formulated in a stochastic programming framework. We propose a solution algorithm based on improved column generation and Benders decomposition with the objective to minimize operational cost and expected recovery cost under a user-defined collection of disruption scenarios. The benefits of our stochastic TAP model are demonstrated with a computational study based on real airline data. Our experimental results highlight the efficiency and effectiveness of our new model.