Abstract
Airline crew rostering is the assignment problem of crew members to planned rotations/pairings for certain month. Airline companies have the monthly task of constructing personalized monthly schedules (roster) for crew members. This problem became more complex and difficult while the aspirations/criterias to assess the quality of roster grew and the constraints increased excessively. This paper proposed the differential evolution (DE) method to solve the airline rostering problem. Different from the common DE, this paper presented random swap as mutation operator. The DE algorithm is proven to be able to find the near optimal solution accurately for the optimization problem. Through numerical experiments with some real datasets, DE showed more competitive results than two other methods, column generation and MOSI (the one used by the Airline). DE produced good results for small and medium datasets, but it still showed reasonable results for large dataset. For large crew rostering problem, we proposed decomposition procedure to solve it in more efficient manner using DE.
Highlights
Development of crews rostering plan which be able to produce the high utility of crews become the priority in human resources department in airline industry
Airline crew rostering is the assignment problem of crew members to planned rotations/pairings for certain month
For large crew rostering problem, we proposed decomposition procedure to solve it in more efficient manner using differential evolution (DE)
Summary
Methods and approaches which are used to solve it are continuously developed to get better result both in optimality side and speed of computational time. Decomposition approach is very effective to solve the difficult and complex problem but this method loss the global treatment since crew pairing and crew rostering done separately. Some other researchers developed the integrated approach to overcome obstacle, such as Souai and Thegem [5], where crew pairing and rostering were done simultantly to get a better optimality level. Many optimization methods have been developed to solve crew scheduling to increase roster quality and to improve computational time such as simulated annealing [11], genetic algorithm [5], tree search algorithm [12], hybrid genetic algorithm [13] and GASA hybrid algorithm [14].
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