Research on Optimization of Heterogeneous Air Cargo Network Based on Hybrid Particle Swarm Algorithm

Abstract

In order to solve the problem of poor timeliness and high operating cost caused by the failure to consider the types of cargo aircrafts and the lack of scientific and reasonable optimization model in the process of transportation scheduling optimization of China’s air cargo network, a transport network optimization model of heterogeneous cargo aircraft formation is established, which takes the total flight cost and the number of all-cargo aircrafts as two optimization objectives. The model takes into account the demand time window of air nodes, the maximum payload constraint of cargo aircrafts, the dynamic loading and unloading time of cargo aircrafts, the take-off and landing time and the handover time of cargo aircrafts. In this paper, a hybrid particle swarm algorithm is proposed. The crossover operator of genetic algorithm is used to keep the directivity of particles, and the acceptance criterion of simulated annealing is used to enhance the ability of the algorithm to jump out of local optimum. The effectiveness of the algorithm and the model is verified by the simulation optimization and comparative analysis of a numerical example.