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.
KeywordsGenetic algorithm Unexpressed genes Crew paring optimization Maximal covering problem
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- 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
- Ceria, S., Nobili, P., Sassano, A. 1998A Lagrangian-based heuristic for large-scale set covering problemsMathematical Programming81215228Google 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.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.Google Scholar
- 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
- 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
- Mahfoud, S.W. 1992Crowding and preselection revisitedProceedings second conference parallel problem solving from nature22736Google Scholar
- Radcliffe, N. J. (1993). Genetic set recombination. Foundations of Genetic Algorithms 2. CA: Morgan Kaufmann.Google Scholar
- Russell, S., Norvig, P. 2003Artificial intelligence: a modern approach 2nd edn.Prentice HallNJGoogle Scholar
- 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