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

摘要： 为了有效监控快递运输过程,对日常快递业务量进行预测,以保证快递包裹能够按时到达。将大量快递包裹运输过程抽象建模以构造多流程实例;提出改进惯性权重的粒子群优化算法和反向传播神经网络的组合模型(IPSO-BP)来预测物流公司日常快递业务量;进而动态申请合适数量云资源以处理变化的业务需求。大量仿真实验证明,在神经网络参数选择合理的情况下,IPSO-BP模型比其他传统方法有更好的预测效果。

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