Solving a Large-Scaled Crew Pairing Problem by Using a Genetic Algorithm

  • Taejin Park
  • Kwang Ryel Ryu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


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.


Genetic Algorithm Tabu Search Crew Schedule Neighborhood Search Algorithm Crew Schedule Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Taejin Park
    • 1
  • Kwang Ryel Ryu
    • 1
  1. 1.Department of Computer EngineeringPusan National UniversityKumjeong-Ku, BusanKorea

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