Short-Haul Airline Crew Rostering by Using Inequality-Based Multiobjective Genetic Algorithm

A novel method of inequality-based multiobjective genetic algorithm (MMGA) is presented for solving the airline crew rostering problem. This approach combines a traditional genetic algorithm with a multiobjective optimization method to address multiple objectives at the same time and to explore the optimal solution. With the method of inequalities, the objective can be modified to update different requirements. Therefore, the proposed MMGA approach has the advantages of global exploration and robustness. There are numerous local optima in airline crew rostering problems, so the problem is challenging to evaluate by using genetic algorithm-based approaches. Computational experiments show that the proposed method can obtain better and more robust results than those of manual rosters made by rosterers who have many years of experience. The research objective was to solve the complex airline crew rostering problem by using the MMGA approach and to demonstrate that this method can reduce the solution time. The proposed method is successfully applied to a domestic short-haul airline. It helps managers find a high-quality, feasible roster and reduce crew-related costs in crew rostering applications.