Project Description

Demand for air transportation has grown very rapidly over the last few decades. This growth can be explained by a generalized increase of population and purchasing power, international business and trade, and also by technological improvements. The growth in demand has not been accompanied with an adequate increase of airport capacity, and this has led to the escalation of congestion problems at many airports worldwide. The airport congestion problems manifest themselves primarily in the form of delays. These delays and their propagation throughout the network have negative impacts on air transportation level of service, on passenger quality of travel, and, more broadly, on economic activity.

Because air transportation is vital for economic activity, there is a need to find ways by which the air transportation system continues to be reliable and meets the increase of demand – it is, thus, important to find solutions to solve the congestion problems at the airports. In the short term, part of these problems can be dealt with through demand management mechanisms. In the longer term, improvements in air traffic control systems will certainly further contribute to attenuate them. However, it is unlikely that airport congestion can be fully coped with if the capacity of existing airports is not expanded and/or new airports are not built.

There are a significant number of (academic) studies dealing with airport expansion and/or location problems, but they focus on individual airports. Studies dealing with airport expansion and/or construction problems from a network perspective are uncommon. This is especially true for the optimization-based literature. This thesis attempts to contribute to this literature by presenting a set of optimization models – from static and deterministic to dynamic and stochastic – for assisting aviation authorities in their strategic reflections regarding the expansion of airport networks. The models apply to a set of metropolitan areas and seek the best improvements to apply to the respective airport network in order to serve demand in the best possible way, for a given budget. The improvements to the airport network are chosen from a set of feasible expansion actions. Expansion actions consist in improvements to the existing airports (through the reconfiguration and/or construction of runways and through the enhancement of terminal buildings) and in the construction of new airports. The objective of the models is to maximize total system throughput (maximize demand “coverage”), taking into account the impact of airport capacity increase on travel costs and travel demand. The models developed are complex mixed-integer nonlinear optimization models, being difficult to solve to exact optimality. Therefore, several heuristic methods are proposed to solve the models. Their performance, from the standpoint of solution quality and computational effort, are compared through their application to a large sample of randomly generated test instances.

The practical usefulness of the models is illustrated with applications to the main airport networks of the United States of America and Germany.

Research Team

CITTA

• Miguel Santos
• António Pais Antunes (supervisor)

MIT

• Amedeo Odoni (supervisor)

Financial Support

• FCT

Stage of Progress

• Concluded in 2012