Journal of Intelligent Manufacturing

, Volume 17, Issue 4, pp 375–383 | Cite as

Crew pairing optimization by a genetic algorithm with unexpressed genes



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.


Genetic algorithm Unexpressed genes Crew paring optimization Maximal covering problem 


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  1. Aickelin, U. 2002An indirect genetic algorithm for set covering problemsJournal of the Operational Research Society5311181126CrossRefGoogle Scholar
  2. Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., Vance, P.H. 1998Branch and price: column generation for huge integer programsOperations Research46316329CrossRefGoogle Scholar
  3. Beasly, J.E., Chu, P.C. 1996A genetic algorithm for the set covering problemEuropean Journal of Operational Research94392404CrossRefGoogle Scholar
  4. Bodin, L., Golden, B., Assad, A., Ball, M. 1983Routing and scheduling of vehicles and crews: the state of the artComputers and Operations Research1063211CrossRefGoogle Scholar
  5. 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.Google Scholar
  6. Ceria, S., Nobili, P., Sassano, A. 1998A Lagrangian-based heuristic for large-scale set covering problemsMathematical Programming81215228Google Scholar
  7. 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.Google Scholar
  8. Emden-Weinert, T., Proksch, M. 1999Best practice simulated annealing for the airline crew scheduling problemJournal of Heuristics5419436CrossRefGoogle Scholar
  9. Fahle, T., Junker, U., Karisch, S.E., Kohl, N., Sellmann, M., Vaaben, B. 2002Constraint programming based column generation for crew assignmentJournal of Heuristics85981CrossRefGoogle Scholar
  10. 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.Google Scholar
  11. 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.Google Scholar
  12. 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.Google Scholar
  13. Lavoie, S., Minoux, M., Odier, E. 1998A new approach for crew pairing problems by column generation with an application to air transportationEuropean Journal of Operations Research354558CrossRefGoogle Scholar
  14. Mahfoud, S.W. 1992Crowding and preselection revisitedProceedings second conference parallel problem solving from nature22736Google Scholar
  15. Radcliffe, N. J. (1993). Genetic set recombination. Foundations of Genetic Algorithms 2. CA: Morgan Kaufmann.Google Scholar
  16. Russell, S., Norvig, P. 2003Artificial intelligence: a modern approach 2nd edn.Prentice HallNJGoogle Scholar
  17. Yoshihara, I. 2003Scheduling of bus driver’s service by a genetic algorithmGhosh, A.Tsutsui, S. eds. Advances in evolutionary computing: theory and applications archiveSpringer-VerlagNew York799817Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Computer EngineeringPusan National UniversityBusanKorea

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