Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abela, J., Abramson, D., Krishnamoorthy, M., de Silva, A., & Mills, G. (1993). Computing optimal schedules for landing aircraft. In Proceedings of the 12th national conference of the Australian society for operations research (pp. 71–90). Adelaide, 7–9 July 1993.
Anagnostakis, I., & Clarke, J.-P. (2002). Runway operations planning, a two-stage heuristic algorithm. In Proceedings of the AIAA aircraft, technology, integration and operations forum, Los Angeles, CA, October 2002.
Anagnostakis, I., & Clarke, J.-P. (2003). Runway operations planning: a two-stage solution methodology. In Proceedings of 36th Hawaii international conference on system sciences (HICSS-36), Hawaii, January 2003.
Anagnostakis, I., Clarke, J.-P., Böhme, D., & Völckers, U. (2001). Runway operations planning and control, sequencing and scheduling. In Proceedings of the 34th Hawaii international conference on system sciences (HICSS-34), Maui, Hawaii, 3–6 January 2001.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2006a). An examination of take-off scheduling constraints at London Heathrow airport. In The 10th international conference on computer-aided scheduling of public transport (CASPT06), Leeds, UK, June 2006.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2006b). The effect of the planning horizon and the freezing time on take-off sequencing. In Proceedings of second international conference on research in air transportation (ICRAT2006), Belgrade, Serbia and Montenegro, June 2006.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2007). Hybrid meta-heuristics to aid runway scheduling at London Heathrow airport. Transportation Science, 41(1), 90–106.
Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). A meta-heuristic approach to aircraft departure scheduling at London Heathrow airport. In M. Hickman, P. Mirchandani, & S. Voss (Eds.), Lecture notes in economics and mathematical systems. Computer aided systems of public transport. Berlin: Springer.
BAA Heathrow (2005–2006). Flight evaluation report 2004/2005. Available at: http://www.heathrowairport.com/assets/B2CPortal/Static%20Files/New2005Booklet.pdf.
Barnhart, C., Belobaba, P., & Odoni, A. R. (2003). Applications of operations research in the air transport industry. Transportation Science, 37(4), 368–391.
Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., & Abramson, D. (2000). Scheduling aircraft landings—the static case. Transportation Science, 34, 180–197.
Beasley, J. E., Sonander, J., & Havelock, P. (2001). Scheduling aircraft landings at London Heathrow using a population heuristic. Journal of the Operational Research Society, 52, 483–493.
Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., & Abramson, D. (2004). Displacement problem and dynamically scheduling aircraft landings. Journal of the Operational Research Society, 55, 54–64.
Bianco, L., Dell’Olmo, P., & Giordani, S. (1999). Minimizing total completion time subject to release dates and sequence-dependent processing times. Annals of Operations Research, 86, 393–416.
Ernst, A. T., Krishnamoorthy, M., & Storer, R. H. (1999). Heuristic and exact algorithms for scheduling aircraft landings. Networks, 34, 229–241.
Gendreau, M., & Potvin, J.-Y. (2005). Tabu search. In E. K. Burke, & G. Kendall (Eds.), Search methodologies (pp. 165–186). Berlin: Springer.
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13, 533–549.
Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic.
Gotteland, J.-B., & Durand, N. (2003). Genetic algorithms applied to airport ground traffic optimization. In Proceedings of the 2003 congress on evolutionary computation, Canberra, ACT.
Idris, H. R., Delcaire, B., Anagnostakis, I., Hall, W. D., Clarke, J.-P., Hansman, R. J., Feron, E., & Odoni, A. R. (1998). Observations of departure processes at Logan airport to support the development of departure planning tools. In Proceedings of the 2nd USA/Europe air traffic management R&D seminar.
Leese, R. A., Craig, A., Ketzscer, R., Noble, S. D., Parrott, K., Preater, J., Wilson, R. E., & Wood, D. A. (2001). The sequencing of aircraft departures. In 40th European study group with industry, Keele.
Newell, G. F. (1979). Airport capacity and delays. Transportation Science, 13(3), 201–241.
Trivizas, D. A. (1998). Optimal scheduling with maximum position shift (MPS) constraints: a runway scheduling application. Journal of Navigation, 51, 250–266.
van Leeuwen, P., Hesselink, H., & Rohling, J. (2002). Scheduling aircraft using constraint satisfaction. In Electronic notes in theoretical computer science (Vol. 76).
Wu, C.-L., & Caves, R. E. (2002). Research review of air traffic management. Transport Reviews, 22(1), 115–132.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Atkin, J.A.D., Burke, E.K., Greenwood, J.S. et al. On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport. J Sched 11, 323–346 (2008). https://doi.org/10.1007/s10951-008-0065-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10951-008-0065-9