Flight graph based genetic algorithm for crew scheduling in airlines
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Crew scheduling is one of the important problems of the airline industry This problem aims to cover a number of flights by crew members, such that all the flights are covered In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences A set of problems of different sizes is generated and solved using the proposed algorithm Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm The results show the superiority of CellDE.