Optimization of Short-Haul Airline Crew Pairing Problems Using a Multiobjective Genetic Algorithm


Airline crew pairing problems involve optimizing an overall evaluation function containing various conflicting objectives and constraints originating from cost and safety considerations. Classical approaches based on set partitioning or set covering methods separate the solution into two phases, pairing generation and pairing optimization, and evaluate the cost by a weighted-sum of objective values. This paper proposes a new multiobjective evolutionary approach to improve the classical solution flow by integrating the two-phase steps as a single step and reasoning the multiple practical objectives simultaneously.
Furthermore, this paper also examines real-life daily pairing problems in a Taiwanese short-haul airline as case studies. Compared to man-made pairing plans, the positive experimental results demonstrate the more appropriate and effective crew pairing plans explored according to practical considerations. These considerations include objectives such as duty connection, transition time, layover, pairing number, aircraft changing limes, flying time, and duty period.