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Airport congestion is a major cause for the large delays that currently affect the air transport industry. These delays have huge cost implications—for the U.S. economy these costs were estimated at $32.9 billion in 2007. In this paper, we present a mixed-integer linear optimization model aimed at assisting airlines in the making of integrated flight scheduling and fleet assignment decisions that take aircraft and passenger delay costs explicitly into account. The objective of the model is to maximize the expected profits of an airline that faces a given origin/destination-based travel demand and operates in congested, slot-constrained airports. Both airline competition and airline cooperation are dealt with in the model, though in a simplified manner. The model was applied to a case study involving the main network of TAP Portugal, which comprises 31 airports and 100 daily flight legs. The results obtained through the model suggest that the Portuguese legacy carrier can improve their expected profits significantly, while diminishing the total number of flights and slightly increasing the passengers' average connecting time. The calculation effort involved in the application of the model even on a desktop computer is small enough to allow its real-time utilization in International Air Transport Association scheduling conferences. These findings clearly indicate that the model is a significant addition to the airline planning toolbox.

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This paper presents an innovative approach to the tactical planning of aircraft remote and contact-stands allocation at airports. We use the concept of recoverable robustness to obtain a recoverable robust solution to the stand allocation problem, a solution that can be recovered by limited means for the included scenarios. Four objective functions are discussed and tested to assess the efficiency of a stand allocation plan. Namely, the minimization of passengers' walking distance, the minimization of tows, the maximization of the number of passengers allocated to contact-stands, and the maximization of the potential airport commercial revenue. The inclusion of an airport commercial revenue metric in the stand allocation optimization model and the comparison of its performance to the pre-mentioned other objectives is another novelty of this work. The research was developed in collaboration with the Guarulhos International Airport of São Paulo for which the recoverable robust approach was tested for 6 days of operations at the airport. We demonstrate that the solutions obtained with the proposed approach outperform the solutions of a traditional robust approach. In addition, a discussion of the trade-off between the different objectives is provided.