Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
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Scheduling Aircraft Landings--The Static Case
Transportation Science
Runway Operations Planning and Control - Sequencing and Scheduling
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Runway Operations Planning: A Two-Stage Solution Methodology
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 3 - Volume 3
Applications of Operations Research in the Air Transport Industry
Transportation Science
Hybrid Metaheuristics to Aid Runway Scheduling at London Heathrow Airport
Transportation Science
A comparison of two methods for reducing take-off delay at London Heathrow airport
Journal of Scheduling
Aircraft sequencing problems via a rolling horizon algorithm
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
A distributed scheduler for air traffic flow management
Journal of Scheduling
Aircraft taxi time prediction: Comparisons and insights
Applied Soft Computing
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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.