Noise load management at Amsterdam Airport Schiphol

Amsterdam Airport Schiphol is one of the five primary hub-airports in Europe. All flight movements are controlled by Air Traffic Control the Netherlands (LVNL), whose main objective is to guarantee safety, efficiency, and protection of the environment, that includes noise load. To this end, a number of enforcement points is located in the vicinity of Schiphol. Each aircraft movement contributes to the noise load at these points. If the cumulative load in an aviation year at an enforcement point exceeds its maximum, the civil aviation authority may impose severe sanctions, such as fines, or a reduction in the number of aircraft movements. The latter is a typical restriction for Schiphol.\ud Runway selection is carried out via the preference list, an ordered set of runway combinations such that the higher on the list a runway combination, the better this combination is for maintaining the noise load limit. The highest safe runway combination in the list will actually be used. This paper has formulated the preference list selection process in the mathematical framework of Stochastic Dynamic Programming that enables determining an optimal strategy for preference list selection taking into account future and unpredictable weather conditions, as well as safety and efficiency restrictions

Stochastic dynamic programming for noise load management

Noise load reduction is among the primary performance targets for some airports. For airports with a complex lay-out of runways, runway selection may then be carried out via a preference list, an ordered set of runway combinations such that the higher on the list a runway combination, the better this combination is for reducing noise load. The highest safe runway combination in the list will actually be used. The optimal preference list selection minimises the probability of exceeding the noise load limit at the end of the aviation year. This paper formulates the preference list selection problem in the framework of Stochastic Dynamic Programming that enables determining an optimal strategy for the monthly preference list selection problem taking into account future and unpredictable weather conditions, as well as safety and efficiency restrictions. The resulting SDP has a finite horizon (aviation year), continuous state space (accumulated noise load), time-inhomogeneous transition densities (monthly weather conditions) and one-step rewards zero. For numerical evaluation of the optimal strategy, we have discretised the state space. In addition, to reduce the size of the state space we have lumped into a single state those states that lie outside a cone of states that may achieve the noise load restrictions. Our results indicate that the SDP approach allows for optimal preference list selection taking into account uncertain weather conditions