Arrival Aircraft Optimal Sequencing based on Teaching-Learning-based Optimization Algorithm with Immunity

Abstract

Sequencing and scheduling of arrival aircrafts is of great significance for ensuring flight safety and reducing flight costs. A multi-constrained approach aircraft optimization ranking model with minimum delay time as an objective function was established. Based on the “Teaching-Learning Based Optimization” (TLBO), the algorithm was discretized, combined with the “antibody injection” and “immune modification” of the Immune Algorithm (IA). The improved algorithm was used to simulate the aircraft sequencing optimization model and compared with the traditional FCFS method. The simulation results show that compared with the FCFS method, the improved TLBO algorithm can significantly reduce the total aircraft delay, effectively mitigate the delay of aircraft arrival, and can be used to solve the problem of aircraft optimization.