Research On Intelligent Flight Scheduling Based On Particle Swarm Optimization

Flight scheduling is a well-known NP-hard problem. Existed algorithms are greatly challenged. It is a complicated problem, which needs to be solved as soon as possible, because it has been puzzling airline companies and passengers for a long time. Particle swarm optimization (PSO), which is one novel kind of mimic algorithm, has become a powerful tool for solving complicated problems. As one kind of useful exploration, this paper will apply particle swarm optimization to intelligent flight scheduling. The main work of this paper is as follows:Based on the research on the model of PSO, this paper analyzes the objective and constraints of multi-flight multi-service scheduling problem, creates the multi-flight multi-service scheduling model, defines a two-dimensional particle representation, puts forward a novel particle swarm optimization to solve the problem, and verifies the effectiveness of the algorithm by simulation experiments. This paper defines the optimization rules of the recovery scheduling of flight delays, applies particle swarm optimization with constriction factor to optimize the recovery scheduling of flight delays, and constructs some tests for different parameters. This paper defines airline factors after considering the economic benefits, social impact and loss constitution of flight delays, creates a new recovery scheduling model of flight delays, and introduces hybrid particle swarm optimization (HPSO) by putting local search method into particle swarm optimization, and then the computation results show that the new model can effectively reduce the losses of flight delays, and the advantages of HPSO, which has higher optimization efficiency than other algorithms, would be more obvious with the increasing of recovery scale in flight delays.