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In order to optimize the recovery scheduling of large-scale flight delays, the loss constitution of flight delays is analyzed after considering the economic benefits and social impact of flight delays, and a new recovery scheduling model of large-scale flight delays is put forward. A hybrid particle swarm optimization (HPSO) is designed by introducing local search method into particle swarm optimization. Simulations are carried out by using flight data from a domestic airport. The results show that HPSO has obvious advantages over other scheduling algorithms, and the purposes of optimizing the sequencing of the flights and reducing the economic losses are achieved.