关键词: 物流运输, 工作流, 粒子群优化算法, 反向传播神经网络, 快递业务量预测

Abstract: To effectively monitor package delivery process,the daily package volume was predicted for ensuring that all packages could reach their destinations on time.A large number of package delivery processes were modeled to construct multi-process instances,and a novel model by combining Improved Particle Swarm Optimization (IPSO) with Back Propagation Neural Network (BPNN) that named IPSO-BP was proposed to predict logistics companies'daily package volume.A suitable number of cloud resources were dynamically allocated for dealing with changing business needs based on the predicted package volume.A large number of experiments indicated that IPSO-BP model had better prediction effect than other conventional methods when neural network parameters were chosen properly.

Key words: logistics transportation, workflow, particle swarm optimization algorithm, back propagation neural network, package volume forecasting