Journal of Scheduling

, Volume 17, Issue 6, pp 601–619 | Cite as

An analysis of constructive algorithms for the airport baggage sorting station assignment problem

  • Amadeo AscóEmail author
  • Jason A. D. Atkin
  • Edmund K. Burke


The assignment of airport resources can significantly affect the quality of service provided by airlines and airports. High quality assignments can support airlines and airports in adhering to published schedules by minimising changes or delays while waiting for resources to become available. In this paper, we consider the problem of assigning available baggage sorting stations to flights which have already been scheduled and allocated to stands. A model for the problem is presented, and the different objectives which have to be considered are highlighted. A number of constructive algorithms for sorting station assignments are then presented and their effects are compared and analysed when different numbers of sorting stations are available. It can be observed that appropriate algorithm selection is highly dependent upon whether or not reductions in service time are permitted and upon the flight density in relation to the number of sorting stations. Finally, since these constructive approaches produce different solutions which are better for different trade-offs of the objectives, we utilise these as initial solutions for an evolutionary algorithm as well as for an Integer Linear Programming model in CPLEX. We show that in both cases they are helpful for improving the results which are obtainable within reasonable solution times.


Airport baggage sorting stations Scheduling Heuristics Constructive algorithms Greedy algorithms 



We are grateful to NATS and EPSRC for providing funding for this project, and especially John Greenwood (NATS) for his strong support.


  1. Abdelghany, A., Abdelghany, K., & Narasimhan, R. (2006). Scheduling baggage-handling facilities in congested airports. Journal of Air Transport Management, 12(2), 76–81. doi: 10.1016/j.jairtraman.2005.10.001.CrossRefGoogle Scholar
  2. Air Transport Users Council (2009). Annual report 2008/09. Tech. Rep., AUC.Google Scholar
  3. Ascó, A., Atkin, J. A. D., & Burke, E. K. (2011). The airport baggage sorting station allocation problem. In J. Fowler, G. Kendall, & B. McCollum (Eds.), Proceedings of the 5th multidisciplinary international conference on scheduling : Theory and applications (MISTA 2011) (pp. 419–444). August 9–11 2011, Phoenix, AZ, USA.
  4. Ascó, A., Atkin, J. A. D., & Burke, E. K. (2012). An evolutionary algorithm for the over-constrained airport baggage sorting station assignment problem. In L. Bui, Y. Ong, N. Hoai, H. Ishibuchi, & P. Suganthan (Eds.), 9th International conference on simulated evolution and learning, SEAL2012, Lecture Notes in Computer Science, Vol. 7673 (pp. 32–41). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-34859-4_4.
  5. Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). On-line decision support for take-off runway scheduling at London Heathrow airport. Journal of Scheduling, 11(5), 323–346. doi: 10.1007/s10951-008-0065-9.CrossRefGoogle Scholar
  6. Atkin, J. A. D., De Maere, G., Burke, E. K., & Greenwood, J. S. (2012). Addressing the pushback time allocation problem at heathrow airport. Transportation Science. doi: 10.1287/trsc.1120.0446.
  7. Bolat, A. (2000). Procedures for providing robust gate assignments for arriving aircrafts. European Journal of Operational Research, 120(1), 63–80. Scholar
  8. Cormen, T. H., Leiserson, C.E., Rivest, R. L., & Stein, C. (Eds.) (2001). Introduction to algorithms. Cambridge, MA: The Massachusetts Institute of Technology Press.
  9. Diepen, G. (2008). Column generation algorithms for machine scheduling and integrated airport planning. PhD thesis, Utrecht University.Google Scholar
  10. Ding, H., Lim, A., Rodrigues, B., & Zhu, Y. (2004). Aircraft and gate scheduling optimization at airports. System Sciences, 2004. Proceedings of the 37th annual Hawaii international conference (pp. 1530–1605). doi: 10.1109/HICSS.2004.1265219.
  11. Ding, H., Rodrigues, A. L., & Zhu, Y. (2005). The over-constrained airport gate assignment problem. Computers and Operations Research, 32(7), 1867–1880.CrossRefGoogle Scholar
  12. Federal Aviation Administration. (2010). FAA aerospace forecast fiscal years 20102030—forecast highlights. Tech. Rep., Federal Aviation Administration.
  13. Haralick, R. M., & Elliott, G. L. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14, 263–313. doi: 10.1016/0004-3702(80)90051-X.CrossRefGoogle Scholar
  14. Hassounah, M. I., & Steuart, G. N. (1993). Demand for aircraft gates. Transportation Research Record, 1423(1423), 26–33.
  15. ICAO (2010). Icao news release—pio 06/10. Tech. Rep., International Civil Aviation Organization.
  16. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operational Research, 43, 254–281. doi: 10.1287/opre.43.2.264.Google Scholar
  17. Nikulin, Y. (2006). Robustness in combinatorial optimization and scheduling theory: An extended annotated bibliography.
  18. Pitt, M., Wai, F. K., & Teck, P. C. (2002). Technology selection in airport passenger and baggage systems. Facilities, 20(10), 314–326. doi: 10.1108/02632770210442992.CrossRefGoogle Scholar
  19. Rijsenbrij, J. C., & Ottjes, J. A. (2007). New developments in airport baggage handling systems. Transportation Planning and Technology, 30(4), 417–430. doi: 10.1080/03081060701461899.CrossRefGoogle Scholar
  20. US Department of Transportation (2010). Air travel consumer report. Tech. Rep., U.S. Department of Transportation.
  21. Voß, S., Fink, A., & Duin, C. (2005). Looking ahead with the pilot method. Annals of Operations Research, 136(1), 285–302. doi: 10.1007/s10479-005-2060-2.CrossRefGoogle Scholar
  22. Wei, D., & Liu, C. (2009). Fuzzy model and optimization for airport gate assignment problem. IEEE international conference on intelligent computing and intelligent systems, ICIS, IEEE, Shanghai, Vol. 2 (pp. 828–832). doi: 10.1109/ICICISYS.2009.5358268.
  23. Wilson, G. C., Intyre, A. M., & Heywood, M. I. (2004). Resource review: Three open source systems for evolving programsLilgp, ECJ and grammatical evolution, Vol. 3. Genetic programming and evolvable machines (pp. 103–105). Hingham, MA: Kluwer Academic Publishers. doi: 10.1023/B:GENP.0000017053.10351.dc.
  24. Wu, C. L., & Caves, R. E. (2000). Aircraft operational costs and turnaround efficiency at airports. Journal of Air Transport Management, 6(4), 201–208. doi: 10.1016/S0969-6997(00)00014-4.CrossRefGoogle Scholar
  25. Wu, C. L., & Caves, R. E. (2004). Modelling and optimization of aircraft turnaround time at an airport. Transportation Planning and Technology, 27(1), 47–66. doi: 10.1080/0308106042000184454.CrossRefGoogle Scholar
  26. Yan, S., & Chang, C. M. (1997). A network model for gate assignment. Journal of Advanced Transportation, 32(2), 176–189. doi: 10.1002/atr.5670320204.CrossRefGoogle Scholar
  27. Yan, S., & Huo, C. M. (2001). Optimization of multiple objective gate assignments. Transportation Research Part A, 35(5), 413–432. doi: 10.1016/S0965-8564(99)00065-8.Google Scholar
  28. Yan, S., Shieh, C. Y., & Chen, M. (2002). A simulation framework for evaluating airport gate assignments. Transportation Research Part A, 36(10), 885–898. doi: 10.1016/S0965-8564(01)00045-3.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Amadeo Ascó
    • 1
    Email author
  • Jason A. D. Atkin
    • 1
  • Edmund K. Burke
    • 2
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Department of Computing and MathematicsUniversity of StirlingStirlingScotland, UK

Personalised recommendations