In view of exiting shortages of the commonly used mathematical model of arrival flights scheduling ( minimum delay time and minimum delay cost), the paper chose four indicators, including air delay cost, passenger delay cost, subsequent delay cost and environmental pollution cost to comprehensively establish an improved mathematical model of minimum delay cost. On the basis of analyzing insufficient ability of seeking optimization and slow convergence speed for the existing particle swarm optimization based on simulated annealing (SA-PSO) algorithm, an annealing strategy of linear differential decrease was applied to SA-PSO (LDD-SA-PSO) algorithm, thereby more effectively solving arrival flights scheduling. The experiment results demonstrated that compared with first come first serve (FCFS), particle swarm optimization (PSO) and SA-PSO, LDD-SA-PSO algorithm has better ability of seeking optimization and convergence speed on the arrival flights scheduling, and that the parameters of improved mathematical model also has obvious influence on the optimization results.
