Skip to main content
SpringerLink
Log in

Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

  • Conference paper
  • First Online:
Advances in Artificial Intelligence (CAEPIA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9868))

Included in the following conference series:

Abstract

Various complex decision making problems are related to airline planning. In the competitive airline industry, efficient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of flight pairings allowing for cost minimizing crew-flight assignments. Experiments are done using a real-life case with different constraints. The results show that our easy-to-use and fast algorithm reduces overall crew flying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aydemir-Karadag, A., Dengiz, B., Bolat, A.: Crew pairing optimization based on hybrid approaches. Comput. Ind. Eng. 65(1), 87–96 (2013)

    Article  Google Scholar 

  2. Clerc, M.: Guided Randomness in Optimization, vol. 1. Wiley-ISTE, London (2006)

    Google Scholar 

  3. Collet, P., Rennard, J.P.: Stochastic optimization algorithms. In: Rennard, J.P. (ed.) Handbook of Research on Nature Inspired Computing for Economics and Management, pp. 28–44. Idea Group Inc., Hershey (2006)

    Google Scholar 

  4. Erdogan, G., Haouari, M., Matogl, M.O., Özener, O.O.: Solving a large-scale crew pairing problem. J. Oper. Res. Soc. 66, 1742–1754 (2015)

    Article  Google Scholar 

  5. Gao, C.: Airline integrated planning and operations. Ph.D. dissertation, Georgia Institute of Technology (2007)

    Google Scholar 

  6. Gopalakrishnan, B., Johnson, E.: Airline crew scheduling: state-of-the-art. Ann. Oper. Res. 140(1), 305–337 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Gruler, A., Juan, A.A., Contreras-Bolton, C., Gatica, G.: A biased-randomized heuristic for the waste collection problem in smart cities. In: Proceedings of the 2015 International Conference of the Forum for Interdisciplinary Mathematics (FIM 2015) (2015)

    Google Scholar 

  8. Juan, A.A., Lourenço, H., Mateo, M., Luo, R., Castella, Q.: Using iterated local search for solving the flow-shop problem: parallelization, parameterization, and randomization issues. Int. Trans. Oper. Res. 21(1), 103–126 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Juan, A.A., Faulin, J., Ferrer, A., Lourenço, H.R., Barrios, B.: MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems. Top 21(1), 109–132 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Juan, A.A., Pascual, I., Guimarans, D., Barrios, B.: Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem. Int. Trans. Oper. Res. 22(4), 647–667 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  11. Klabjan, D., Lee, Y.C., Stojković, G.: Crew management information systems. In: Barnhart, C., Smith, B. (eds.) Quantitative Problem Solving Methods in the Airline Industry, pp. 237–282. Springer, New York (2012)

    Chapter  Google Scholar 

  12. Martí, R., Resende, M., Ribeiro, C.C.: Multi-start methods for combinatorial optimization. Eur. J. Oper. Res. 226(1), 1–8 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Medard, C.P., Sawhney, N.: Airline crew scheduling from planning to operations. Eur. J. Oper. Res. 38(3), 1013–1027 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Muter, I., Birbil, S.I., Bülbül, K., Şahin, G., Yenigün, H., Taş, D., Tüzün, D.: Solving a robust airline crew pairing problem with column generation. Comput. Oper. Res. 40(3), 815–830 (2013)

    Article  Google Scholar 

  15. Mohamed, N.F., Zainuddin, Z.M., Salhi, S., Mohamed, N.H., Mohamed, N.A.: A heuristic and exact method: integrated aircraft routing and crew pairing problem. Mod. Appl. Sci. 10(4), 128–136 (2016)

    Article  Google Scholar 

  16. Papadakos, N.: Integrated airline scheduling. Comput. Oper. Res. 36, 176–195 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Resende, M.G.C., Ribeiro, C.C.: GRASP: greedy randomized adaptive search procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249. Springer, New York (2003)

    Google Scholar 

  18. Salazar-González, J.J.: Approaches to solve the fleet-assignment, aircraft-routing, crew-pairing and crew-rostering problems of a regional carrier. Omega 43, 71–82 (2014)

    Article  Google Scholar 

  19. Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, London (2006)

    MATH  Google Scholar 

  20. Tekiner, H.: Robust crew pairing for managing extra flights. Master’s thesis (2006)

    Google Scholar 

  21. Vance, P.H., Barnhart, C., Johnson, E.L., Nemhauser, G.L.: Airline crew scheduling: a new formulation and decomposition algorithm. Oper. Res. 45(2), 183–200 (1997)

    Article  MATH  Google Scholar 

  22. Yan, S., Tung, T.T., Tu, Y.P.: Optimal construction of airline individual crew pairings. Comput. Oper. Res. 29, 341–363 (2002)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Public University of Navarra, Pamplona, Spain

    Alba Agustín

  2. Open University of Catalonia, Barcelona, Spain

    Aljoscha Gruler, Jesica de Armas & Angel A. Juan

Authors
  1. Alba Agustín
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aljoscha Gruler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jesica de Armas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Angel A. Juan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aljoscha Gruler .

Editor information

Editors and Affiliations

  1. Artificial Intelligence Center, University of Oviedo, Gijón, Spain

    Oscar Luaces

  2. University of Castilla-La Mancha , Albacete, Spain

    José A. Gámez

  3. Public University of Navarre , Pamplona, Spain

    Edurne Barrenechea

  4. Universidad Pablo de Olavide , Sevilla, Spain

    Alicia Troncoso

  5. Public University of Navarre , Pamplona, Navarra, Spain

    Mikel Galar

  6. University of Salamanca , Salamanca, Spain

    Héctor Quintián

  7. University of Salamanca , Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Agustín, A., Gruler, A., de Armas, J., Juan, A.A. (2016). Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44636-3_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44635-6

  • Online ISBN: 978-3-319-44636-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation