A metaheuristic approach to aircraft departure scheduling at London Heathrow airport

London Heathrow airport is one of the busiest airports in the world. Moreover, it is unusual among the world's leading airports in that it only has two runways. At many airports the runway throughput is the bottleneck to the departure process and, as such, it is vital to schedule departures effectively and efficiently. For reasons of safety, separations need to be enforced between departing aircraft. The minimum separation between any pair of departing aircraft is determined not only by those aircraft but also by the flight paths and speeds of aircraft that have previously departed. Departures from London Heathrow are subject to physical constraints that are not usually addressed in departure runway scheduling models. There are many constraints which impact upon the orders of aircraft that are possible and we will show how these constraints either have already been included in the model we present or can be included in the future. The runway controllers are responsible for the sequencing of the aircraft for the departure runway. This is currently carried out manually. In this paper we propose a metaheuristic-based solution for determining good sequences of aircraft in order to aid the runway controller in this difficult and demanding task. Finally some results are given to show the effectiveness of this system and we evaluate those results against manually produced real world schedules

A comparative historical model for analyzing disagreements about penal policy

SIGLEGBUnited Kingdo

On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport

This paper addresses the challenge of building an automated decision support methodology to tackle the complex problem faced every day by runway controllers at London Heathrow Airport. Aircraft taxi from stands to holding areas at the end of the take-off runway where they wait in queues for permission to take off. A runway controller attempts to find the best order for aircraft to take off. Sequence-dependent separation rules that depend upon aircraft size, departure route and speed group ensure that this is not a simple problem to solve. Take-off time slots on some aircraft and the need to avoid excessive delay for any aircraft make this an even more complicated problem. Making this decision at the holding area helps to avoid the problems of unpredictable push-back and taxi times, but introduces a number of complex spatial constraints that would not otherwise exist. The holding area allows some flexibility for interchange of aircraft between queues, but this is limited by its physical layout. These physical constraints are not usually included in academic models of the departure problem. However, any decision support system to support the take-off runway controller must include them. We show, in this paper, that a decision support system could help the controllers to significantly improve the departure sequence at busy times of the day, by considering the taxiing aircraft in addition to those already at the holding area. However, undertaking this re-introduces the issue of taxi time uncertainty, the effect of which we explicitly measure in these experiments. Empirical results are presented for experiments using real data from different times of the day, showing how the performance of the system varies depending upon the volume of traffic and the accuracy of the provided taxi time estimations. We conclude that the development of a good taxi time prediction system is key to maximising the benefits, although benefits can be observed even without this