Solving a Large-Scaled Crew Pairing Problem by Using a Genetic Algorithm
This paper presents an algorithm for a crew pairing optimization, which is an essential part of crew scheduling. The algorithm first generates many pairings and then finds their best subset by a genetic algorithm which incorporates unexpressed genes. The genetic algorithm used employs greedy crossover and mutation operators specially designed to work with chromosomes of set-oriented representation. As a means of overcoming the premature convergence problem caused by greedy genetic operators, the chromosome is made up of an expressed part and an unexpressed part. The presented method was tested on real crew scheduling data.
KeywordsGenetic Algorithm Tabu Search Crew Schedule Neighborhood Search Algorithm Crew Schedule Problem
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- 2.Caparara, A., Fischetti, M., Guida, P.L., Toth, P., Vigo, D.: Solution of large-scale railway crew planning problems: The Italian experience. Technical Report OR-97-9, DEIS University of Bologna (1997)Google Scholar
- 5.Hwang, J., Kang, C.S., Ryu, K.R., Han, Y., Choi, H.R.: A Hybrid of Tabu Search and Integer Programming for Subway Crew Scheduling Optimization. In: IASTED-ASC, pp. 72–77 (2002)Google Scholar
- 7.Crawford, K.D., Hoelting, C.J., Wainwright, R.L., Schoenefeld, D.A.: A study of fixed-length subset recombination. In: Foundations of Genetic Algorithm 4 (1996)Google Scholar
- 8.Radcliffe, N.J.: Genetic set recombination. In: Foundations of Genetic Algorithms 2, vol. 2. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 9.Mahfoud, S.W.: Crowding and preselection revisited. In: Proceedings Second Conference Parallel Problem Solving from Nature (1992)Google Scholar
- 10.Goldberg, D.E., Richardson, J.: Genetic algorithm with sharing for multimodal function optimization. In: Proceedings of the Second International conference on Genetic Algorithm, pp. 41–49 (1987)Google Scholar