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

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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.

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References

  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.

    Article  Google Scholar 

  2. Gladkov, V.A., Kureichik, V.V., and Kureichik, V.M., Geneticheskie algoritmy (Genetic Algorithms), Moscow: Fizmatlit, 2006, 2nd ed.

    Google Scholar 

  3. Stevens, G., An Approach to Scheduling Aircraft Landing Times Using Genetic Algorithms, Melbourne: RMIT Univ., 1995.

    Google Scholar 

  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. 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. 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.

    Article  MATH  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    MATH  Google Scholar 

  9. Beasley, J.E., OR-Library: Distributing Test Problems by Electronic Mail, J. Operat. Res. Soc., 1990, vol. 41, no. 11, pp. 1069–1072.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  18. Larrañaga, P. and Lozano, J.A., Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, New York: Springer, 2001.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

  23. Faye, A., Solving the Aircraft Landing Problem with Time Discretization Approach, Eur. J. Operat. Res., 2015, vol. 242, no. 3, pp. 1028–1038.

    Article  MathSciNet  MATH  Google Scholar 

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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.

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Correspondence to G. S. Veresnikov, N. A. Egorov, E. L. Kulida or V. G. Lebedev.

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Russian Text © The Author(s), 2018, published in Problemy Upravleniya, 2018, No. 5, pp. 2–13.

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Veresnikov, G.S., Egorov, N.A., Kulida, E.L. et al. Methods for Solving of the Aircraft Landing Problem. II. Approximate Solution Methods. Autom Remote Control 80, 1502–1518 (2019). https://doi.org/10.1134/S0005117919080101

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