Skip to main content

Advertisement

SpringerLink
Log in

Crew pairing optimization by a genetic algorithm with unexpressed genes

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

We propose a genetic algorithm to solve the pairing optimization problem for subway crew scheduling. Our genetic algorithm employs new crossover and mutation operators specially designed to work with the chromosomes of set-oriented representation. To enhance the efficiency of the search with the newly designed genetic operators, we let a chromosome consist of an expressed part and an unexpressed part. While the genes in both parts evolve, only the genes in the expressed part are used when an individual is evaluated. The purpose of the unexpressed part is to preserve information susceptible to be lost by the application of genetic operators, and thus to maintain the diversity of the search. Experiments with real-world data have shown that our genetic algorithm outperforms other local search methods such as simulated annealing and tabu search.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • U. Aickelin (2002) ArticleTitleAn indirect genetic algorithm for set covering problems Journal of the Operational Research Society 53 IssueID10 1118–1126 Occurrence Handle10.1057/palgrave.jors.2601317

    Article  Google Scholar 

  • C. Barnhart E.L. Johnson G.L. Nemhauser M.W.P. Savelsbergh P.H. Vance (1998) ArticleTitleBranch and price: column generation for huge integer programs Operations Research 46 316–329 Occurrence Handle10.1287/opre.46.3.316

    Article  Google Scholar 

  • J.E. Beasly P.C. Chu (1996) ArticleTitleA genetic algorithm for the set covering problem European Journal of Operational Research 94 392–404 Occurrence Handle10.1016/0377-2217(95)00159-X

    Article  Google Scholar 

  • L. Bodin B. Golden A. Assad M. Ball (1983) ArticleTitleRouting and scheduling of vehicles and crews: the state of the art Computers and Operations Research 10 63–211 Occurrence Handle10.1016/0305-0548(83)90030-8

    Article  Google Scholar 

  • Caparara, A., Fischetti, M., Guida, P.L., Toth, P., & Vigo, D. (1997). Solution of large-scale railway crew planning problems: the Italian experience. Technical Report OR-97–9, DEIS University of Bologna.

  • S. Ceria P. Nobili A. Sassano (1998) ArticleTitleA Lagrangian-based heuristic for large-scale set covering problems Mathematical Programming 81 215–228

    Google Scholar 

  • Crawford, K.D., Hoelting, C.J., Wainwright, R.L., & Schoenefeld, D.A. (1996). A study of fixed-length subset recombination. Foundsations of Genetic Algorithm, 4, 1996.

  • T. Emden-Weinert M. Proksch (1999) ArticleTitleBest practice simulated annealing for the airline crew scheduling problem Journal of Heuristics 5 IssueID4 419–436 Occurrence Handle10.1023/A:1009632422509

    Article  Google Scholar 

  • T. Fahle U. Junker S.E. Karisch N. Kohl M. Sellmann B. Vaaben (2002) ArticleTitleConstraint programming based column generation for crew assignment Journal of Heuristics 8 IssueID1 59–81 Occurrence Handle10.1023/A:1013613701606

    Article  Google Scholar 

  • Goldberg, D.E., & Richardson, J. (1987). Genetic algorithm with sharing for multimodal function optimization. Proceedings of the second international conference on genetic algorithm, pp. 41–49.

  • Hwang, J., Kang, C. S., Ryu, K. R., Han, Y., & Choi, H. R. (2002). A hybrid of tabu search and integer programming for subway crew scheduling optimization. IASTED-ASC, pp. 72–77.

  • Kornilakis, H., & Stamatopoulos, P. (2002). Crew pairing optimization with genetic algorithms. Proceedings of the second hellenic conference on AI: methods and applications of artificial, pp. 109–120.

  • S. Lavoie M. Minoux E. Odier (1998) ArticleTitleA new approach for crew pairing problems by column generation with an application to air transportation European Journal of Operations Research 35 45–58 Occurrence Handle10.1016/0377-2217(88)90377-3

    Article  Google Scholar 

  • S.W. Mahfoud (1992) ArticleTitleCrowding and preselection revisited Proceedings second conference parallel problem solving from nature 2 27–36

    Google Scholar 

  • Radcliffe, N. J. (1993). Genetic set recombination. Foundations of Genetic Algorithms 2. CA: Morgan Kaufmann.

  • S. Russell P. Norvig (2003) Artificial intelligence: a modern approach 2nd edn. Prentice Hall NJ

    Google Scholar 

  • I. Yoshihara (2003) Scheduling of bus driver’s service by a genetic algorithm A. Ghosh S. Tsutsui (Eds) Advances in evolutionary computing: theory and applications archive Springer-Verlag New York 799–817

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Pusan National University, San 30, Jangjeon-Dong, Guemjeong-Ku, Busan, 609-735, Korea

    Taejin Park & Kwang Ryel Ryu

Authors
  1. Taejin Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwang Ryel Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taejin Park.

Additional information

Received: June 2005/Accepted: December 2005

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, T., Ryu, K.R. Crew pairing optimization by a genetic algorithm with unexpressed genes. J Intell Manuf 17, 375–383 (2006). https://doi.org/10.1007/s10845-005-0011-z

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-005-0011-z

Keywords

Search

Navigation