A Real-Time Decision-Support Tool for the Integrated Airline Recovery using a Machine Learning Approach

Airlines frequently deal with unexpected disruptions, which have to be resolved in order to resume their operations again. Decision-support tools help airlines with disruption management. However, long computational times are associated with integrated recovery models that compute globally optimal solutions, which makes these tools unfit for...

Multi-period adaptive fleet planning problem: with Approximate Dynamic Programming

This MSc thesis presents a stochastic modelling approach to the multi-period airline fleet planning problem. Approximate Dynamic Programming (ADP) is used to model the impact of demand uncertainty on fleet decisions. The proposed ADP algorithm applies local value function approximations resulting from Gaussian kernel regressions to estimate...