Skip to main content
Log in

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

  • S.I.: CLAIO 2014
  • Published:
Annals of Operations Research Aims and scope Submit manuscript


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.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others


  • 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.

    Article  Google Scholar 

  • Alefragis, P., Sanders, P., Takkula, T., & Wedelin, D. (2000). Parallel integer optimization for crew scheduling. Annals of Operations Research, 99(1–4), 141–166.

    Article  Google Scholar 

  • Anbil, R., Gelman, E., Patty, B., & Tanga, R. (1991). Recent advances in crew-pairing optimization at American Airlines. Interfaces, 21(1), 62–74.

    Article  Google Scholar 

  • Belobaba, P., Odoni, A., & Barnhart, C. (Eds.). (2009). The global airline industry. New York: Wiley.

    Google Scholar 

  • 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.

  • Cappanera, P., & Gallo, G. (2004). A multicommodity flow approach to the crew rostering problem. Operations Research, 52(4), 583–596.

    Article  Google Scholar 

  • Chu, H. D., Gelman, E., & Johnson, E. L. (1997). Solving large scale crew scheduling problems. European Journal of Operational Research, 97(2), 260–268.

    Article  Google Scholar 

  • Dawid, H., König, J., & Strauss, C. (2001). An enhanced rostering model for airline crews. Computers & Operations Research, 28(7), 671–688.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

  • Gopalakrishnan, B., & Johnson, E. L. (2005). Airline crew scheduling: State-of-the-art. Annals of Operations Research, 140(1), 305–337.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Irnich, S., & Desaulniers, G. (2005). Shortest path problems with resource constraints. Column Generation. New York: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kasirzadeh, A., Saddoune, M., & Soumis, F. (2014). Airline crew scheduling: Models, algorithms, and data sets. Gerad, Montreal.

  • Kohl, N., & Karisch, S. E. (2004). Airline crew rostering: Problem types, modeling, and optimization. Annals of Operations Research, 127(1–4), 223–257.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Nicoletti, B. (1975). Automatic crew rostering. Transportation Science, 9, 33–42.

    Article  Google Scholar 

  • NBAA Management Guide. (2014). Last access 28 Jan 2016.

  • Ryan, D. M. (1993). The solution of massive generalized set partitioning problems in aircrew rostering. Journal of the Operational Research Society, 43, 459–467.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Söhlke, A., & Nowak, I. (2007). The crew scheduling problem: Solution methods and their application in the airline industry. Lufthansa systems, EURO XXII prague. Last access 17 May 2016.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Angel A. Juan.



This “Appendix” extends the data provided in the experimental section to better illustrate the concepts. It also shows details of solutions from the airline and from our algorithm.

Fig. 4
figure 4

Daily flight schedule

Fig. 5
figure 5

Aircraft routing

Fig. 6
figure 6

Crew pairings

Fig. 7
figure 7

Crew rostering solution implemented by the airline

Fig. 8
figure 8

Solution to the crew rostering problem given by our first approach

Fig. 9
figure 9

Solution to the crew rostering problem given by our second approach

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Armas, J., Cadarso, L., Juan, A.A. et al. A multi-start randomized heuristic for real-life crew rostering problems in airlines with work-balancing goals. Ann Oper Res 258, 825–848 (2017).

Download citation

  • Published:

  • Issue Date:

  • DOI: