Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Multiobjective genetic algorithm to solve the train crew scheduling problem
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
Ant colony optimization-based algorithm for airline crew scheduling problem
Expert Systems with Applications: An International Journal
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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.