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.
In this paper, the cross-entropy (CE) method is proposed to solve non-linear discriminant analysis or Kernel Fisher discriminant (CE-KFD) analysis. CE through certain steps can find the optimal or near optimal solution with a fast rate of convergence for optimization problem. While, KFD is to solve problem of Fisher's linear discriminant in a kernel feature space F by maximizing between class variance and minimizing within class variance. Through the numerical experiments, we found that CE-KFD demonstrates the high accuracy of the results compared to the traditional methods, Fisher LDA and kernel Fisher (KFD) with eigen decomposition method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.