Optimization of short-haul airline crew pairing problems using a multiobjective genetic algorithm

[[abstract]]Airline crew pairing problems involve optimizing an overall evaluation func-tion containing various conflicting objectives and constraints originating from cost and safety considerations. Classical approaches based on set partitioning or set covering meth-ods 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 integrat-ing 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 ex-perimental results demonstrate the more appropriate and effective crew pairing plans ex-plored according to practical considerations. These considerations include objectives such as duty connection, transition time, layover, pairing number, aircraft changing times, fly-ing time, and duty period