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Annals of Operations Research

, Volume 258, Issue 2, pp 825–848 | Cite as

A multi-start randomized heuristic for real-life crew rostering problems in airlines with work-balancing goals

  • Jesica de Armas
  • Luis Cadarso
  • Angel A. Juan
  • Javier Faulin
S.I.: CLAIO 2014

Abstract

This paper proposes a multi-start randomized heuristic for solving real-life crew rostering problems in airlines. The paper describes realistic constrains, regulations, and rules that have not been considered in the literature so far. Our algorithm is designed to provide quality solutions satisfying these real-life specifications while, at the same time, it aims at balancing the workload distribution among the different crewmembers. Thus, our approach promotes corporate social responsibility by distributing the workload in a fair way and avoiding that some crewmembers get unnecessarily overstressed. Despite its importance in real-life applications, these aspects have seldom been considered in the crew scheduling literature, where most solving approaches refer to simplified models and are tested on non-realistic benchmarks. The experimental tests show that our algorithm is capable of generating feasible quality solutions to real-life crew rostering problems in just a few seconds. These times are orders of magnitude lower than the times currently employed by some airlines to obtain a single feasible solution, since the ‘optimal’ solutions provided by most commercial software usually require additional adjustments in order to meet all the real-life specifications.

Keywords

Air transportation Crew rostering problem Randomized heuristics 

Notes

Acknowledgments

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Ibero-American Program for Science and Technology for Development (CYTED2014-515RT0489). Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001) and the CAN Foundation in Navarre, Spain (CAN2015-3963).

References

  1. Achour, H., Gamache, M., Soumis, F., & Desaulniers, G. (2007). An exact solution approach for the preferential bidding system problem in the airline industry. Transportation Science, 41(3), 354–365.CrossRefGoogle Scholar
  2. Alefragis, P., Sanders, P., Takkula, T., & Wedelin, D. (2000). Parallel integer optimization for crew scheduling. Annals of Operations Research, 99(1–4), 141–166.CrossRefGoogle Scholar
  3. Anbil, R., Gelman, E., Patty, B., & Tanga, R. (1991). Recent advances in crew-pairing optimization at American Airlines. Interfaces, 21(1), 62–74.CrossRefGoogle Scholar
  4. Belobaba, P., Odoni, A., & Barnhart, C. (Eds.). (2009). The global airline industry. New York: Wiley.Google Scholar
  5. Barnhart, C., Cohn, A. M, Johnson, E. L., Klabjan, D., Nemhauser, G. L., & Vance, P. H. (2003). Airline crew scheduling. In: R. W. Hall (Ed.), Handbook of transportation science, International series in operations research & management science (Vol. 56, pp. 517–560). New York: Springer.Google Scholar
  6. Cappanera, P., & Gallo, G. (2004). A multicommodity flow approach to the crew rostering problem. Operations Research, 52(4), 583–596.CrossRefGoogle Scholar
  7. Chu, H. D., Gelman, E., & Johnson, E. L. (1997). Solving large scale crew scheduling problems. European Journal of Operational Research, 97(2), 260–268.CrossRefGoogle Scholar
  8. Dawid, H., König, J., & Strauss, C. (2001). An enhanced rostering model for airline crews. Computers & Operations Research, 28(7), 671–688.CrossRefGoogle Scholar
  9. Deng, G. F., & Lin, W. T. (2011). Ant colony optimization-based algorithm for airline crew scheduling problem. Expert Systems with Applications, 38(5), 5787–5793.CrossRefGoogle Scholar
  10. Desaulniers, G., Desrosiers, J., Solomon, M. M., Soumis, F., & Villeneuve, D. (1998). A unified framework for deterministic time constrained vehicle routing and crew scheduling problems. New York: Springer.CrossRefGoogle Scholar
  11. El Moudani, W., Cosenza, C. A. N., De Coligny, M., & Mora-Camino, F. (2001). A bi-criterion approach for the airlines crew rostering problem. In E. Zitzler, L. Thiele, K. Deb, C.A Coello Coello, & D. Corne (Eds.), Evolutionary multi-criterion optimization (pp. 486–500). Berlin, Heidelberg: Springer.Google Scholar
  12. Gopalakrishnan, B., & Johnson, E. L. (2005). Airline crew scheduling: State-of-the-art. Annals of Operations Research, 140(1), 305–337.CrossRefGoogle Scholar
  13. Gamache, M., Soumis, F. (1998). A method for optimally solving the rostering problem. In G. Yu (Ed.), International series in operations research & management science (Vol. 9, pp. 124–157). New York: Springer.Google Scholar
  14. Gamache, M., Soumis, F., Marquis, G., & Desrosiers, J. (1999). A column generation approach for large-scale aircrew rostering problems. Operations Research, 47(2), 247–263.CrossRefGoogle Scholar
  15. Irnich, S., & Desaulniers, G. (2005). Shortest path problems with resource constraints. Column Generation. New York: Springer.Google Scholar
  16. Juan, A., Faulin, J., Ferrer, A., Lourenço, H., & Barrios, B. (2013a). MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems. TOP, 21, 109–132.CrossRefGoogle Scholar
  17. Juan, A., Faulin, J., Jorba, J., Caceres, J., & Marques, J. (2013b). Using parallel & distributed computing for solving real-time vehicle routing problems with stochastic demands. Annals of Operations Research, 207, 43–65.CrossRefGoogle Scholar
  18. Kasirzadeh, A., Saddoune, M., & Soumis, F. (2014). Airline crew scheduling: Models, algorithms, and data sets. Gerad, Montreal. https://www.gerad.ca/en/papers/G-2014-22
  19. Kohl, N., & Karisch, S. E. (2004). Airline crew rostering: Problem types, modeling, and optimization. Annals of Operations Research, 127(1–4), 223–257.CrossRefGoogle Scholar
  20. Lučic, P., & Teodorovic, D. (1999). Simulated annealing for the multi-objective aircrew rostering problem. Transportation Research Part A: Policy and Practice, 33(1), 19–45.Google Scholar
  21. Maenhout, B., & Vanhoucke, M. (2010). A hybrid scatter search heuristic for personalized crew rostering in the airline industry. European Journal of Operational Research, 206(1), 155–167.CrossRefGoogle Scholar
  22. Nicoletti, B. (1975). Automatic crew rostering. Transportation Science, 9, 33–42.CrossRefGoogle Scholar
  23. NBAA Management Guide. (2014). http://www.nbaa.org/admin/management-guide/. Last access 28 Jan 2016.
  24. Ryan, D. M. (1993). The solution of massive generalized set partitioning problems in aircrew rostering. Journal of the Operational Research Society, 43, 459–467.CrossRefGoogle Scholar
  25. Salazar-González, J. J. (2014). Approaches to solve the fleet-assignment, aircraft-routing, crew-pairing and crew-rostering problems of a regional carrier. Omega, 43, 71–82.CrossRefGoogle Scholar
  26. Söhlke, A., & Nowak, I. (2007). The crew scheduling problem: Solution methods and their application in the airline industry. Lufthansa systems, EURO XXII prague. https://www.lhsystems.com/fileadmin/user_upload/files/en/information/EURO_XXII_xOPT.pdf. Last access 17 May 2016.
  27. Souai, N., & Teghem, J. (2009). Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem. European Journal of Operational Research, 199(3), 674–683.Google Scholar
  28. Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., & De Boeck, L. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226(3), 367–385.CrossRefGoogle Scholar
  29. Wedelin, D. (1995). An algorithm for large scale 0–1 integer programming with application to airline crew scheduling. Annals of operations research, 57(1), 283–301.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jesica de Armas
    • 1
  • Luis Cadarso
    • 2
  • Angel A. Juan
    • 1
  • Javier Faulin
    • 3
  1. 1.Computer Science, Multimedia, and Telecommunication DepartmentOpen University of Catalonia – IN3BarcelonaSpain
  2. 2.European Institute for Aviation Training and Accreditation (EIATA)Rey Juan Carlos UniversityFuenlabradaSpain
  3. 3.Department of Statistics and Operations ResearchCampus Arrosadia - Public University of NavarrePamplonaSpain

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