关键词: 粒子群优化, 机场车辆, 调度, 梯度降低

Abstract: With the rapid development of aviation, the airport security and efficiency of the vehicle scheduling status has been increasingly highlighted, the traditional airport vehicle scheduling uses the strategy of First in First out , this strategy algorithm is simple, easy to implement, defect is all scheduling group being treated the same, cannot provide delay guarantee for real-time demanding business, algorithm with impartiality. This paper proposes an improved airport vehicle scheduling model based on optimization particle swarm , the particle swarm as a whole has to search the global optimal location as a special particle, using gradient descent strategy optimization of the particle, global optimization characteristics and optimization neighborhood of the gradient descent algorithm, feature fusion, to enhance the efficiency of particle swarm optimization algorithm of global optimization, reduce vehicle scheduling calculation time at the airport. Simulation experiments show that the particle swarm optimization to improve the airport vehicle scheduling model, can reduce the number of traditional scheduling method optimization iterations, and shorten the optimal operation time, effectively relieve air blockage caused by resources waste.

Key words: particle swarm optimization, airport vehicles, scheduling, gradient descent