Advertisement

Automation and Remote Control

, Volume 80, Issue 8, pp 1502–1518 | Cite as

Methods for Solving of the Aircraft Landing Problem. II. Approximate Solution Methods

  • G. S. VeresnikovEmail author
  • N. A. EgorovEmail author
  • E. L. KulidaEmail author
  • V. G. LebedevEmail author
Control Sciences
  • 1 Downloads

Abstract

Methods are considered of an approximate solution of the static problem of forming the optimal aircraft queue for landing, which do not guarantee an accurate solution but provide an opportunity to obtain an acceptable solution that meets the requirements. It is noted that typically they are a synthesis of a meta-heuristic method of global optimization to obtain the landing sequence of aircraft and a local exact method to find the optimal solution for the sequences obtained. The brief overview of some of them is presented.

Keywords

optimal queue for landing objective function genetic algorithms global and local optimization memetic algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

This work was supported in part by the Russian Foundation for Basic Research (project no. 18-08-00822) and the Program I.30 of the Presidium of the Russian Academy of Sciences.

References

  1. 1.
    Veresnikov, G.S., Egorov, N.A., Kulida, E.L., and Lebedev, V.G., Methods for Solving of the Aircraft Landing Problem. I. Exact Solution Methods, Autom. Remote Control, 2019, vol. 80, no. 7, pp. 1317–1334.Google Scholar
  2. 2.
    Gladkov, V.A., Kureichik, V.V., and Kureichik, V.M., Geneticheskie algoritmy (Genetic Algorithms), Moscow: Fizmatlit, 2006, 2nd ed.Google Scholar
  3. 3.
    Stevens, G., An Approach to Scheduling Aircraft Landing Times Using Genetic Algorithms, Melbourne: RMIT Univ., 1995.Google Scholar
  4. 4.
    Kumar, N.K. and Kumar, R., A comparative Analysis of PMX, CX and OX Crossover Operators for Solving Traveling Salesman Problem, Int. J. Latest Res. Sci. Techn., 2012, vol. 1, no. 2, pp. 98–101.Google Scholar
  5. 5.
    Ciesielski, V. and Scerri, P., Real Time Genetic Scheduling of Aircraft Landing Times, in 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, Anchorage, USA: IEEE, 1998, pp. 360–364.Google Scholar
  6. 6.
    Beasley, J.E., Krishnamoorthy, M., Sharaiha, Y.M., and Abramson, D., Scheduling Aircraft Landings— The Static Case, Transport. Sci., 2000, vol. 34, no. 2, pp. 180–197.zbMATHGoogle Scholar
  7. 7.
    Beasley, J.E., Sonander, J., and Havelock, P., Scheduling Aircraft Landings at London Heathrow Using a Population Heuristic, J. Operat. Res. Soc., 2001, vol. 52, no. 5, pp. 483–493.zbMATHGoogle Scholar
  8. 8.
    Beasley, J.E. and Pinol, H., Scatter Search and Bionomic Algorithms for the Aircraft Landing Problem, Eur. J. Operat. Res., 2006, vol. 127, no. 2, pp. 439–462.zbMATHGoogle Scholar
  9. 9.
    Beasley, J.E., OR-Library: Distributing Test Problems by Electronic Mail, J. Operat. Res. Soc., 1990, vol. 41, no. 11, pp. 1069–1072.Google Scholar
  10. 10.
    Bencheikh, G. and Khoukhi, F., Hybrid Algorithms for the Multiple Runway Aircraft Landing Problem, Int. J. Computer Sci. Appl., 2013, vol. 10, no. 2, pp. 53–71.Google Scholar
  11. 11.
    Bencheikh, G., Boukachour, J., and Alaoui, A.H., Improved Ant Colony Algorithm to Solve the Aircraft Landing Problem, Int. J. Computer Theory Eng., 2011, vol. 3, no. 2, pp. 224–233.Google Scholar
  12. 12.
    Chastikova, V.A. and Vlasov, K.A., Development and Comparative Analysis of Heuristic Algorithms to Search for the Minimal Hamiltonian Cycle in The Complete Graph, Fundam. Issl., 2013, vol. 10, pp. 63–66.Google Scholar
  13. 13.
    Semenkina, O.E. and Semenkin, E.S., On Effectiveness Comparison of Ant Colony and Genetic Algorithms for Solving Combinatorial Optimization Problems, Aktual. Probl. Aviats. Kosmonavt., 2011, vol. 1, no. 7, pp. 338–339.Google Scholar
  14. 14.
    Bencheikh, G., Boukachour, J., and Alaoui, A.H., A Memetic Algorithm to Solve the Dynamic Multiple Runway Aircraft Landing Problem, J. King Saud Univ., Comput. Informat. Sci., 2016, vol 28, no. 1, pp. 98–109.Google Scholar
  15. 15.
    Bo Xu, An Efficient Ant Colony Algorithm Based onWake-VortexModeling Method for Aircraft Scheduling Problem, J. Comput. Appl. Math., 2017, vol. 317, pp. 157–170.MathSciNetGoogle Scholar
  16. 16.
    Xiao-Peng Ji, Xian-Bin Cao, and Ke Tang, Sequence Searching and Evaluation: A Unified Approach for Aircraft Arrival Sequencing and Scheduling Problems, Memetic Computing, 2016, vol. 8, no. 2, pp. 109–123.Google Scholar
  17. 17.
    Hu, X. and Paolo, E., A Ripple-Spreading Genetic Algorithm for the Aircraft Sequencing Problem, Evolut. Comput., 2011, vol. 19, no. 1, pp. 77–106.Google Scholar
  18. 18.
    Larrañaga, P. and Lozano, J.A., Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, New York: Springer, 2001.zbMATHGoogle Scholar
  19. 19.
    Ceberio, J., Irurozki, E., Mendiburu, A., et al., A Distance-Based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem, IEEE Trans. Evolut. Comput., 2014, vol. 18, no. 2, pp. 286–300.Google Scholar
  20. 20.
    Ceberio, J., Irurozki, E., Mendiburu, A., and Lozano, J.A., A Review on Estimation of Distribution Algorithms in Permutation-Based Combinatorial Optimization Problems, Progress Artific. Intell., 2012, vol. 1, no. 1, pp. 103–117.Google Scholar
  21. 21.
    Ceberio, J., Mendiburu, A., and Lozano, J.A., Introducing the Mallows Model on Estimation of Distribution Algorithms, Int. Conf. on Neural Information Processing, Berlin: Springer, 2011, pp. 461–470.Google Scholar
  22. 22.
    Moscato, P., On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms, C3P Report 826, California Institute of Technology, Pasadena, Caltech Concurrent Computation Program, 1989.Google Scholar
  23. 23.
    Faye, A., Solving the Aircraft Landing Problem with Time Discretization Approach, Eur. J. Operat. Res., 2015, vol. 242, no. 3, pp. 1028–1038.MathSciNetzbMATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia

Personalised recommendations