Joint Optimisation of the Gate and Taxi Route During Peak Hours: A Fusion Algorithm of Adaptive Genetic and Artificial Bee Colony

The joint optimisation of parking gates and taxiing time is critical in improving airport operation efficiency. However, there is a lack of effective solutions to this problem, especially during peak hours. This paper constructs a joint optimisation model for parking gate and taxiing route assignment based on column generation (CG). An artificial bee colony–adaptive genetic algorithm fusion is designed to solve the joint model considering CG characteristics and can operate in parallel. Then this paper uses the actual data from Beijing Capital International Airport (PEK) and Kunming Changshui International Airport (KMG) to verify the proposed method. It is proved that this study can effectively improve parking gate and taxiway utilisation compared with the actual allocation results and baseline algorithms. Meanwhile, in peak hours, this method can still give stable results at an acceptable time.