Advertisement

Allocation of Airport Check-in Counters Using a Simulation-Optimization Approach

Chapter

Abstract

The aviation industry is expected to grow at a pace of 4 % per annum in the coming years, therefore it is necessary to have techniques that support the management of the resources at hand in the best possible way so that facility expansion is delayed as much as possible with the corresponding capital savings. This chapter presents a methodology that combines evolutionary algorithms and simulation for performing the allocation of the check-in desks in such a way that the different stochastic and deterministic variables are taken into account for a more robust solution. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance at the counters, opening times of check-in desks, and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as passenger arrival profiles, passenger profile, layout of the facility, among others, with the purpose of determining an airport terminal’s check-in area which allocation is the most efficient in real situations to keep the quality indicators at the desired level. The example presented is for an airport terminal’s check-in area, but the methodology can be used for similar allocation problems in the aviation industry and in other industries such as logistics or manufacturing.

Keywords

Evolutionary Algorithm Constraint Satisfaction Problem Flight Plan Terminal Area Integer Programming Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the Aviation Academy of the HvA, the Mexican Council for Science and Research (CONACYT) and the Popular Autonomous University of Puebla for their support for this work.

References

  1. 1.
    BOEING. Current market outlook 2013 2032. commercial airplanes market analysis, 2013.Google Scholar
  2. 2.
    EUROCONTROL. Challenges of growth: summary report, 2013.Google Scholar
  3. 3.
    AIRBUS S.A.S. Global market forecast. future journeys 2013 2032, 2013.Google Scholar
  4. 4.
    A.R. Correia, S.C. Wirasinghe, and A.G. Barros. Overall level of service measures for airport passenger terminals. Transportation Research Part A: Policy and Practice, 42(2):330–346, 2008.Google Scholar
  5. 5.
    R. Neufville, A.R. Odoni, Belobaba P.P., and T.G. Reynolds, editors. Airport Systems: Planning, Design, and Management. McGraw-Hill Education: New York, Chicago, San Francisco, Lisbon, London, Madrid, Mexico City, Milan, New Delhi, San Juan, Seoul, Singapore, Sydney, Toronto, 2013.Google Scholar
  6. 6.
    S. Delahaye, D., Puechmorel. Modeling and Optimization of Air Traffic. Wiley-ISTE, 2013.Google Scholar
  7. 7.
    F. Glover and G.A. Kochenberger. Handbook of Metaheuristics. Kluwer Academic Publishers, 2003.Google Scholar
  8. 8.
    KingLoong Shiu and K.Y. Szeto. Self-adaptive Mutation Only Genetic Algorithm: An Application on the Optimization of Airport Capacity Utilization, volume 5326 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008.Google Scholar
  9. 9.
    Thomas Black. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford, UK, 1996.Google Scholar
  10. 10.
    D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.Google Scholar
  11. 11.
    M. Affenzeller, S. Winkler, A. Beham, and S. Wagner. On the influence of selection schemes on the genetic diversity in genetic algorithms.In Roberto Moreno-Daz, Franz Pichler, and Alexis Quesada-Arencibia, editors, Computer Aided Systems Theory - EUROCAST 2009, volume 5717 of Lecture Notes in Computer Science, pages 777–784. Springer Berlin Heidelberg, 2009.CrossRefGoogle Scholar
  12. 12.
    J.R Koza. Genetic Programming. MIT press, 1992.Google Scholar
  13. 13.
    Z. Michalewicz. Genetic algorithms + Data Structures = Evolution Programs. Springer-verlag, 1992.Google Scholar
  14. 14.
    H.P. Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995.Google Scholar
  15. 15.
    J. Banks, Carson J.S., and B.L. Nelson. Discrete Event System Simulation. Prentice Hall, Upper Saddle River, NJ, 1996.Google Scholar
  16. 16.
    K. Jensen and L.M. Kristensen. Coloured Petri Nets: Modeling and Validation of Concurrent Systems. Kluwer Academic Publishers, 2009.Google Scholar
  17. 17.
    SIMIO. Simio homepage, 2002.Google Scholar
  18. 18.
    ARENA. Arena homepage, 2002.Google Scholar
  19. 19.
    F. Longo. Advances of modeling and simulation in supply chain and industry. Simulation, 87(8):651–656, aug 2011.Google Scholar
  20. 20.
    Juan Ignacio Latorre, Emilio Jiménez, and Mercedes Pérez. The optimization problem based on alternatives aggregation petri nets as models for industrial discrete event systems. Simulation, 89(3):346–361, March 2013.Google Scholar
  21. 21.
    S. Yan, C.Y. Shieh, and M. Chen. A simulation framework for evaluating airport gate assignments. Transportation Research Part A: Policy and Practice, 36(10):885–898, 2002.Google Scholar
  22. 22.
    R.E. Kazda, A.; Caves. Airport Design and Operation. Emerald, Inc., 2007.Google Scholar
  23. 23.
    B. Giuseppe and A. Genovese. A mathematical model for the optimization of the airport check-in service problem. Electronic Notes in Discrete Mathematics, 36(0):703–710, 2010. {ISCO} 2010 - International Symposium on Combinatorial Optimization.Google Scholar
  24. 24.
    C.I. Hsu, C.C. Chao, and K.Y. Shih. Dynamic allocation of check-in facilities and dynamic assignment of passengers at air terminals. Computers & Industrial Engineering, 63(2):410–417, 2012.Google Scholar
  25. 25.
    M. Parlar, B. Rodrigues, and M. Sharafali. On the allocation of exclusive-use counters for airport check-in queues: static vs. dynamic policies. OPSEARCH, 50(3):433–453, 2013.CrossRefGoogle Scholar
  26. 26.
    M. Parlar and M. Sharafali. Dynamic allocation of airline check-in counters: A queueing optimization approach. Management Science, 54(8):1410–1424, 2008.Google Scholar
  27. 27.
    R.A. Littler and D. Whitaker. Estimating staffing requirements at an airport terminal. Journal of the Operational Research Society, 48(2):124–131, 1997.Google Scholar
  28. 28.
    H.W. Chun and R. W.T. Mak. Intelligent resource simulation for an airport check-in counter allocation system. Trans. Sys. Man Cyber Part C, 29(3):325–335, August 1999.Google Scholar
  29. 29.
    Y. Park and S.B. Ahn. Optimal assignment for check-in counters based on passenger arrival behaviour at an airport. Transportation Planning and Technology, 26(5):397–416, 2003.Google Scholar
  30. 30.
    S. Yan, C.H. Tang, and M. Chen. A model and a solution algorithm for airport common use check-in counter assignments. Transportation Research Part A: Policy and Practice, 38(2):101–125, 2004.Google Scholar
  31. 31.
    N.M. van Dijk and E. van der Sluis. Check-in computation and optimization by simulation and ip in combination. European Journal of Operational Research, 171(3):1152–1168, 2006.Google Scholar
  32. 32.
    J.I. Castillo-Manzano and L. Lopez.-Valpuesta. Check–in services and passenger behaviour: Self service technologies in airport systems. Computers in Human Behavior, 29(6):2431–2437, 2013.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Aviation AcademyAmsterdam University of Applied SciencesAmsterdamThe Netherlands
  2. 2.Logistics & Supply Chain Management DepartmentUniversidad Popular Autonoma del Estado de PueblaPueblaMexico

Personalised recommendations