Advertisement

Greedy Based Pareto Local Search for Bi-objective Robust Airport Gate Assignment Problem

  • Wenxue Sun
  • Xinye CaiEmail author
  • Chao Xia
  • Muhammad Sulaman
  • Mustafa Mısır
  • Zhun Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

The present paper proposes a Greedy based Pareto Local Search (GB-PLS) algorithm for the bi-objective robust airport gate assignment problem (bRAGAP). The bRAGAP requires to minimize the total passenger walking distance and the total robust cost of gate assignment. The robust cost is measured through our proposed evaluation function considering the impact of delay cost on the allocation of idle time. GB-PLS uses the Random and Greedy Move (RGM) as a neighborhood search operator to improve the convergence and diversity of the solutions. Two populations are maintained in GB-PLS: the external population (EP) stores the nondominated solutions and the starting population (SP) maintains all the starting solutions for Pareto local search (PLS). The PLS is applied to search the neighborhood of each solution in the SP and the generated solutions are used to update the EP. A number of extensive experiments has been conducted to validate the performance of GB-PLS over Pareto Simulated Annealing (PSA).

Keywords

Bi-objective optimization Robust airport gate assignment Pareto Local Search Neighborhood search 

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61300159, by the Natural Science Foundation of Jiangsu Province of China under grant BK20130808 and by China Postdoctoral Science Foundation under grant 2015M571751.

References

  1. 1.
    Drexl, A., Nikulin, Y.: Multicriteria airport gate assignment and pareto simulated annealing. IIE Trans. 40(4), 385–397 (2007)CrossRefGoogle Scholar
  2. 2.
    Dorndorf, U., Drexl, A., Nikulin, Y., Pesch, E.: Flight gate scheduling: state-of-the-art and recent developments. Omega 35(3), 326–334 (2007)CrossRefGoogle Scholar
  3. 3.
    Ding, H., Lim, A., Rodrigues, B., Zhu, Y.: The over-constrained airport gate assignment problem. Comput. Oper. Res. 32(7), 1867–1880 (2005)CrossRefzbMATHGoogle Scholar
  4. 4.
    Yan, S., Huo, C.M.: Optimization of multiple objective gate assignments. Transp. Res. Part A Policy Pract. 35(5), 413–432 (2001)CrossRefGoogle Scholar
  5. 5.
    Ding, H., Lim, A., Rodrigues, B., Zhu, Y.: Aircraft and gate scheduling optimization at airports. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences, pp. 74–81. IEEE (2004)Google Scholar
  6. 6.
    Ding, H., Lim, A., Zhu, Y.: New heuristics for over-constrained flight to gate assignments. J. Oper. Res. Soc. 55(7), 760–768 (2004)CrossRefzbMATHGoogle Scholar
  7. 7.
    Nikulin, Y., Drexl, A.: Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models. J. Sched. 13(3), 261–280 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Gupet, J., Acuna-Agost, R., Briant, O., Gayon, J.P.: Exact and heuristic approaches to the airport stand allocation problem. Eur. J. Oper. Res. 246(2), 597–608 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Dorndorf, U., Jaehn, F., Pesch, E.: Flight gate assignment and recovery strategies with stochastic arrival and departure times. OR Spectrum 39(1), 65–93 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India (2000)Google Scholar
  11. 11.
    Lust, T.: Speed-up techniques for solving large-scale bTSP with the two-phase pareto local search. In: Genetic and Evolutionary Computation Conference (GECCO), Atlanta, USA, pp. 761–762. ACM Press (2008)Google Scholar
  12. 12.
    Lust, T., Teghem, J.: Two-phase pareto local search for the biobjective traveling salesman problem. J. Heuristics 16(3), 475–510 (2010)CrossRefzbMATHGoogle Scholar
  13. 13.
    Xu, J., Bailey, G.: The Airport Gate Assignment Problem: Mathematical Model and a Tabu Search Algorithm (2001)Google Scholar
  14. 14.
    Bolat, A.: Assigning arriving flights at an airport to the available gates. J. Oper. Res. Soc. 50(1), 23–34 (1999)CrossRefzbMATHGoogle Scholar
  15. 15.
    Bolat, A.: Procedures for providing robust gate assignments for arriving aircrafts. Eur. J. Oper. Res. 120(1), 63–80 (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Lim, A., Wang, F.: Robust airport gate assignment. In: IEEE International Conference on TOOLS with Artificial Intelligence, pp. 74–81 (2005)Google Scholar
  17. 17.
    Diepen, G., Akker, J.M.V.D., Hoogeveen, J.A., Smeltink, J.W.: Finding a robust assignment of flights to gates at amsterdam airport schiphol. J. Sched. 15(6), 703–715 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Castaing, J., Mukherjee, I., Cohn, A., Hurwitz, L., Nguyen, A., Ller, J.J.: Reducing airport gate blockage in passenger aviation. Comput. Oper. Res. 65(C), 189–199 (2014)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Xu, X., Xiong, L.: Cost analysis of flight delays and simulation in ground-holding model. J. Nanjing Univ. Aeronaut. Astronaut. 38(1), 115–120 (2006)zbMATHGoogle Scholar
  20. 20.
    Zhao, X.L., Zhu, J.F., Mei, G.: Study on modelling and algorithm of irregular flight delay operation. Syst. Eng.-Theory Pract. 4, 018 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wenxue Sun
    • 1
  • Xinye Cai
    • 1
    Email author
  • Chao Xia
    • 1
  • Muhammad Sulaman
    • 1
  • Mustafa Mısır
    • 1
  • Zhun Fan
    • 2
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of EngineeringShantou UniversityGuangdongChina

Personalised recommendations