Scheduling aircraft landings at London Heathrow using a population heuristic

With increasing levels of air traffic, making effective use of limited airport capacity is obviously important. This paper reports on an investigation undertaken by National Air Traffic Services in the UK into improving runway utilisation at London Heathrow. This investigation centred on developing an algorithm for improving the scheduling of aircraft waiting to land. The heuristic algorithm developed (a population heuristic) is discussed and results presented using actual operational data relating to aircraft landings at London Heathrow. This data indicates that our algorithm could have improved on air traffic control decisions in such cases by between 2–5 % in terms of reducing the timespan required to land all of the aircraft considered.

[1]  K. Al-Sultan,et al.  A Genetic Algorithm for the Set Covering Problem , 1996 .

[2]  Janić Milan,et al.  The flow management problem in air traffic control: a model of assigning priorities for landings at a congested airport , 1997 .

[3]  Pablo Moscato,et al.  Handbook of Applied Optimization , 2000 .

[4]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[5]  Lucio Bianco,et al.  Minimizing total completion time subject to release dates and sequence‐dependentprocessing times , 1999, Ann. Oper. Res..

[6]  Jean-Marc Rousseau,et al.  Chapter 5 Models in urban and air transportation , 1994, Operations research and the public sector.

[7]  Scott Robert Ladd,et al.  Genetic algorithms in C , 1995 .

[8]  J. Beasley Population Heuristics , 1999 .

[9]  J. Mullins,et al.  TRAILS OF DESTRUCTION , 1996 .

[10]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[11]  John E. Beasley,et al.  A Genetic Algorithm for the Multidimensional Knapsack Problem , 1998, J. Heuristics.

[12]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  David Abramson,et al.  Scheduling Aircraft Landings - The Static Case , 2000, Transp. Sci..

[15]  Andreas T. Ernst,et al.  Heuristic and exact algorithms for scheduling aircraft landings , 1999, Networks.

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[17]  John E. Beasley,et al.  A genetic algorithm for the generalised assignment problem , 1997, Comput. Oper. Res..

[18]  Colin R. Reeves,et al.  Genetic Algorithms for the Operations Researcher , 1997, INFORMS J. Comput..

[19]  F. Glover,et al.  In Modern Heuristic Techniques for Combinatorial Problems , 1993 .

[20]  Yazid M. Sharaiha,et al.  Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..

[21]  John E. Beasley,et al.  Constraint Handling in Genetic Algorithms: The Set Partitioning Problem , 1998, J. Heuristics.