Annals of Operations Research

, Volume 204, Issue 1, pp 249–270 | Cite as

Airport runway scheduling

  • Julia A. Bennell
  • Mohammad Mesgarpour
  • Chris N. Potts
Article

Abstract

Airport runway optimization is an ongoing challenge for air traffic controllers. Since demand for air-transportation is predicted to increase, there is a need to realize additional take-off and landing slots through better runway scheduling. In this paper, we review the techniques and tools of operational research and management science that are used for scheduling aircraft landings and take-offs. The main solution techniques include dynamic programming, branch and bound, heuristics and meta-heuristics.

Keywords

Aircraft landing problem Aircraft take-off problem Scheduling Sequencing Separation time 

Notes

Acknowledgement

This work has been co-financed by the European Organisation for the Safety of Air Navigation (EUROCONTROL) under its Research Grant scheme. The content of the work does not necessarily reflect the official position of EUROCONTROL on the matter.

©2011, EUROCONTROL and the University of Southampton. All Rights reserved.

References

  1. Abela, J., Abramson, D., Krishnamoorthy, M., De Silva, A., & Mills, G. (1993). Computing optimal schedules for landing aircraft. In Proceedings of 12th national conference of the Australian Society for Operations Research, Adelaide, Australia (pp. 71–90). Google Scholar
  2. Anagnostakis, I. (2004). A multi-objective, decomposition-based algorithm design methodology and its application to runway operations planning. PhD thesis, Department of Aeronautics & Astronautics, MIT, USA. Google Scholar
  3. Anagnostakis, I., & Clarke, J.-P. (2002). Runway operations planning: a two-stage heuristic algorithm. In AIAA aircraft, technology, integration and operations forum, Los Angeles, CA, USA. Google Scholar
  4. Anagnostakis, I., & Clarke, J.-P. (2003). Runway operations planning: a two-stage solution methodology. In Proceedings of the 36th annual Hawaii international conference on system science (HICSS’03), Big Island, HI, USA. Google Scholar
  5. Artiouchine, K., Baptiste, P., & Durr, C. (2008). Runway sequencing with holding patterns. European Journal of Operational Research, 189, 1254–1266. CrossRefGoogle Scholar
  6. Atkin, J. A. D. (2008). On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport. PhD thesis, University of Nottingham, UK. Google Scholar
  7. Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2004). A metaheuristic approach to aircraft departure scheduling at London Heathrow airport. In Electronic proceedings of the 9th international conference on computer-aided scheduling of public transport, San Diego, California, USA. Google Scholar
  8. Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2006). An examination of take-off scheduling constraints at London Heathrow airport. In Electronic proceedings of the 10th international conference on computer-aided scheduling of public transport (CASPT2006). Google Scholar
  9. Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2007). Hybrid metaheuristics to aid runway scheduling at London Heathrow airport. Transportation Science, 41, 90–106. CrossRefGoogle Scholar
  10. Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport. Journal of Scheduling, 11, 323–346. CrossRefGoogle Scholar
  11. Balakrishnan, H., & Chandran, B. (2006). Scheduling aircraft landings under constrained position shifting. In AIAA guidance, navigation and control conference and exhibit, Keystone, Colorado, USA. Google Scholar
  12. Balakrishnan, H., & Chandran, B. (2007). Efficient and equitable departure scheduling in real-time: new approaches to old problems. In USA/Europe air traffic management R&D seminar, Barcelona, Spain. Google Scholar
  13. Bauerle, N., Engelhardt-Funke, O., & Kolonko, M. (2007). On the waiting time of arriving aircraft and the capacity of airports with one or two runways. European Journal of Operational Research, 177, 1180–1196. CrossRefGoogle Scholar
  14. Bayen, A. M., Tomlin, C. J., Ye, Y., & Zhang, J. (2004). An approximation algorithm for scheduling aircraft with holding time. In 43rd IEEE conference on decision and control, Atlantis, Paradise Island, Bahamas. Google Scholar
  15. Bazargan, M., Fleming, K., & Subramanian, P. (2002). A simulation study to investigate runway capacity using TAAM. In Proceedings of the 34th winter simulation conference (WSC’02), San Diego, California, USA. Google Scholar
  16. Beasley, J. E. (1990). OR-library: distributing test problems by electronic mail. Journal of the Operational Research Society, 41, 1069–1072. Google Scholar
  17. Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., & Abramson, D. (2000). Scheduling aircraft landings—the static case. Transportation Science, 34, 180–197. CrossRefGoogle Scholar
  18. 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. CrossRefGoogle Scholar
  19. Beasley, J. E., Sonander, J., & Havelock, P. (2001). Scheduling aircraft landing at London Heathrow using a population heuristic. Journal of the Operational Research Society, 52, 483–493. CrossRefGoogle Scholar
  20. Bencheikh, G., Boukachour, J., Alaoui, A. E. H., & Khoukhi, F. E. (2009). Hybrid method for aircraft landing scheduling based on a job shop formulation. International Journal of Computer Science and Network Security, 9, 78–88. Google Scholar
  21. Bennell, J. A., Mesgarpour, M., & Potts, C. N. (2011). Airport runway scheduling. 4OR, 9, 115–138. CrossRefGoogle Scholar
  22. Bianco, L., & Bielli, M. (1993). System aspects and optimization models in ATC planning. In L. Bianco & A. R. Odoni (Eds.), Large scale computation and information processing in ATC (pp. 47–100). Berlin: Springer. Google Scholar
  23. Bianco, L., Dell’Olmo, P., & Giordani, S. (1997). Scheduling models and algorithms for TMA traffic management. In L. Bianco, P. Dell’Olmo, & A. R. Odoni (Eds.), Modelling and simulation for air traffic management (pp. 139–167). Berlin, Heidelberg: Springer. CrossRefGoogle Scholar
  24. 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, 393–415. CrossRefGoogle Scholar
  25. Bianco, L., Dell’Olmo, P., & Giordani, S. (2006). Scheduling models for air traffic control in terminal areas. Journal of Scheduling, 9, 223–253. CrossRefGoogle Scholar
  26. Blumstein, A. (1959). The landing capacity of a runway. Operations Research, 7, 752–763. CrossRefGoogle Scholar
  27. Brentnall, A. R. (2006). Aircraft arrival management. PhD thesis, University of Southampton, UK. Google Scholar
  28. Brentnall, A. R., & Cheng, R. C. H. (2009). Some effects of aircraft arrival sequence algorithms. Journal of the Operational Research Society, 60, 962–972. CrossRefGoogle Scholar
  29. Brinton, C. R. (1992). An implicit enumeration algorithm for arrival aircraft scheduling. In Proceedings of the IEEE/AIAA 11th digital avionics systems conference, Seattle, WA, USA. Google Scholar
  30. Capri, S., & Ignaccolo, M. (2004). Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model. Journal of Air Transport Management, 10, 345–351. CrossRefGoogle Scholar
  31. Carr, G. C., Erzberger, H., & Neuman, F. (2000). Fast-time study of airline-influenced arrival sequencing and scheduling. Journal of Guidance, Control, and Dynamics, 23, 526–531. CrossRefGoogle Scholar
  32. Chandran, B., & Balakrishnan, H. (2007). A dynamic programming algorithm for robust runway scheduling. In Proceedings of the American control conference, New York, NY, USA. Google Scholar
  33. Cheng, V. H. L., Crawford, L. S., & Menon, P. K. (1999). Air traffic control using genetic search techniques. In Proceedings of the IEEE international conference on control applications, Hawaii, HA, USA. Google Scholar
  34. Ciesielski, V., & Scerri, P. (1997). An anytime algorithm for scheduling of aircraft landing times using genetic algorithms. Australian Journal of Intelligent Information Processing Systems, 4, 206–213. Google Scholar
  35. Ciesielski, V., & Scerri, P. (1998). Real time genetic scheduling of aircraft landing times. In D. Fogel (Ed.), Proceedings of the 1998 IEEE international conference on evolutionary computation (ICEC’98), Anchorage, Alsaka (pp. 360–364). Google Scholar
  36. Craig, A., Ketzscher, R., Leese, R. A., 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, UK. Google Scholar
  37. de Neufville, R., & Odoni, A. R. (2003). Airport systems: planning, design, and management. New York: McGraw-Hill. Google Scholar
  38. Dear, R. (1976). The dynamic scheduling of aircraft in the near terminal area (Technical Report, R76-9). Flight Transportation Laboratory, MIT, USA. Google Scholar
  39. Dear, R. G., & Sherif, Y. S. (1989). The dynamic scheduling of aircraft in high density terminal areas. Microelectrons and Reliability, 29, 743–749. CrossRefGoogle Scholar
  40. Dear, R. G., & Sherif, Y. S. (1991). An algorithm for computer assisted sequencing and scheduling of terminal area operation. Transportation Research. Part A: General, 25, 129–139. CrossRefGoogle Scholar
  41. Duke, G. (2009). Air traffic control (10th ed.). UK: Ian Allan. Google Scholar
  42. Ernst, A. T., Krishnamoorthy, M., & Storer, R. H. (1999). Heuristic and exact algorithms for scheduling aircraft landings. Networks, 34, 229–241. CrossRefGoogle Scholar
  43. EUROCONTROL (2009). SESAR Web site. Google Scholar
  44. FAA (2009). NextGen Web site. Google Scholar
  45. Fahle, T., Feldmann, R., Gotz, S., Grothklags, S., & Monien, B. (2003). The aircraft sequencing problem. In Computer science in perspective, LNCS (Vol. 2598, pp. 152–166). CrossRefGoogle Scholar
  46. Filar, J. A., Manyem, P., & White, K. (2001). How airlines and airports recover from schedule perturbations: a survey. Annals of Operations Research, 108, 315–333. CrossRefGoogle Scholar
  47. Hansen, J. V. (2004). Genetic search methods in air traffic control. Computers & Operations Research, 3, 445–459. CrossRefGoogle Scholar
  48. Hu, X.-B., & Chen, W.-H. (2005a). Genetic algorithm based on receding horizon control for arrival sequencing and scheduling. Engineering Applications of Artificial Intelligence, 18, 633–642. CrossRefGoogle Scholar
  49. Hu, X.-B., & Chen, W.-H. (2005b). Receding horizon control for aircraft arrival sequencing and scheduling. IEEE Transactions on Intelligent Transportation Systems, 6, 189–197. CrossRefGoogle Scholar
  50. Hu, X.-B., & Di Paolo, E. (2008). Binary-representation-based genetic algorithm for aircraft arrival sequencing and scheduling. IEEE Transactions on Intelligent Transportation Systems, 9, 301–310. CrossRefGoogle Scholar
  51. Hu, X.-B., & Di Paolo, E. (2009). An efficient genetic algorithm with uniform crossover for air traffic control. Computers & Operations Research, 36, 245–259. CrossRefGoogle Scholar
  52. Idris, H. R. (2001). Observation and analysis of departure operations at Boston Logan international airport. PhD thesis, Department of Mechanical Engineering, MIT, USA. Google Scholar
  53. Idris, H. R., Delcaire, B., Anagnostakis, I., Hall, W. D., Clarke, J.-P., Hansman, R. J., Feron, E., & Odoni, A. R. (1998a). Observations of departure processes at Logan airport to support the development of departure planning tools. In The 2nd USA/Europe air traffic management R&D seminar, Orlando, USA. Google Scholar
  54. Idris, H. R., Delcaire, B., Anagnostakis, I., Hall, W. D., & Pujet, N. (1998b). In Identification of flow constraint and control points in departure operations at airport systems. In AIAA guidance, navigation, and control conference and exhibit, Boston, MA, USA. Google Scholar
  55. Isaacson, D. R., Davis, T. J., & Robinson, J. E. (1997). Knowledge-based runway assignment for arrival aircraft in the terminal area. In Proceedings of AIAA guidance, navigation, and control conference, New Orleans, LA, USA. Google Scholar
  56. Lee, H., & Balakrishnan, H. (2008). Fuel cost, delay and throughput tradeoffs in runway scheduling. In Proceedings of American control conference (ACC 08), Seattle, Washington, USA. Google Scholar
  57. Luenberger, R. A. (1998). A traveling-salesman-based approach to aircraft scheduling in the terminal area (Technical report, NASA Technical Memorandum 100062). Google Scholar
  58. Mesgarpour, M. (2012). Airport runway optimization. PhD thesis, School of Mathematics, University of Southampton, UK. Google Scholar
  59. Mesgarpour, M., Potts, C. N., & Bennell, J. A. (2010). Models for aircraft landing optimization. In Proceedings of the 4th international conference on research in air transportation (ICRAT2010), Budapest, Hungary. Google Scholar
  60. Moser, I., & Hendtlass, T. (2007). Solving dynamic single-runway aircraft landing problems with extremal optimization. In Proceedings of the 2007 IEEE symposium on computational intelligence in scheduling (CI-Sched 2007), Honolulu, Hawaii, USA. Google Scholar
  61. Neuman, F., & Erzberger, H. (1991). Analysis of delay reducing and fuel saving sequencing and spacing algorithms for arrival traffic (Technical report, TM-103880). Ames Research Center, NASA, USA. Google Scholar
  62. Newell, G. F. (1979). Airport capacity and delays. Transportation Science, 13, 201–241. CrossRefGoogle Scholar
  63. Pinol, H., & Beasley, J. E. (2006). Scatter search and bionomic algorithms for the aircraft landing problem. European Journal of Operational Research, 171, 439–462. CrossRefGoogle Scholar
  64. Psaraftis, H. N. (1978). A dynamic programming approach to the aircraft sequencing problem (Technical report, R78-4). Flight Transportation Laboratory, MIT, USA. Google Scholar
  65. Psaraftis, H. N. (1980). A dynamic programming approach for sequencing groups of identical jobs. Operations Research, 28, 1347–1359. CrossRefGoogle Scholar
  66. Pujet, N., Delcaire, B., & Feron, E. (1999). Input-output modelling and control of the departure process of congested airports. In Proceedings of AIAA guidance, navigation and control conference, and exhibit, Portland, OR, USA. Google Scholar
  67. Randall, M. C. (2002). Scheduling aircraft landings using ant colony optimisation. In Proceedings of the IASTED international conference artificial intelligence and soft computing, Banff, Canada. Google Scholar
  68. Soomer, M. J., & Franx, G. J. (2008). Scheduling aircraft landings using airlines’ preferences. European Journal of Operational Research, 190, 277–291. CrossRefGoogle Scholar
  69. Stevens, G. (1995). An approach to scheduling aircraft landing times using genetic algorithms. Honours thesis, Department of Computer Science, RMIT University, Melbourne, Australia. Google Scholar
  70. Tang, K., Wang, Z., Cao, X., & Zhang, J. (2008). A multi-objective evolutionary approach to aircraft landing scheduling. In Proceedings of IEEE congress on evolutionary computation, CEC 2008, Hong Kong. Google Scholar
  71. Tether, B. S., & Metcalfe, J. S. (2003). Horndal at Heathrow? Capacity creation through co-operation and system evolution. Industrial and Corporate Change, 12, 437–476. CrossRefGoogle Scholar
  72. Trivizas, D. A. (1998). Optimal scheduling with maximum position shift (MPS) constraints: a runway scheduling application. Journal of Navigation, 51, 250–266. CrossRefGoogle Scholar
  73. van Leeuwen, P., Hesselink, H., & Rohling, J. (2002). Scheduling aircraft using constraint satisfaction. Electronic Notes in Theoretical Computer Science, 76, 252–268. CrossRefGoogle Scholar
  74. van Leeuwen, P., & van Hanxleden Houwert, N. (2003). Scheduling aircraft using constraint relaxation. In Proceedings of the 22nd workshop of the UK planning and scheduling special interest group, Glasgow, UK. Google Scholar
  75. Veidal, E. (2007). Scheduling aircraft landings—the dynamic case. Master’s thesis, Department of Informatics and Mathematical Modelling, Technical University of Denmark. Google Scholar
  76. Wen, M. (2005). Algorithms of scheduling aircraft landing problem. Master’s thesis, Department of Informatics and Mathematical Modelling, Technical University of Denmark. Google Scholar
  77. Wen, M., Larsen, J., & Clausen, J. (2005). An exact algorithm for aircraft landing problem (IMM-Technical Report-2005-12). Informatics and Mathematical Modelling, Technical University of Denmark. Google Scholar
  78. Yu, S., Cao, X., Hu, M., Du, W., & Zhang, J. (2009). A real-time schedule method for aircraft landing scheduling problem based on cellular automaton. In Proceedings of the 1st ACM/SIGEVO summit on genetic and evolutionary computation, Shanghai, China. Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Julia A. Bennell
    • 1
  • Mohammad Mesgarpour
    • 2
  • Chris N. Potts
    • 2
  1. 1.CORMSIS, School of ManagementUniversity of SouthamptonSouthamptonUK
  2. 2.CORMSIS, School of MathematicsUniversity of SouthamptonSouthamptonUK

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