物流配送成本优化估计的数学模型研究

发布时间：2018-06-29 20:54

本文选题：物流配送 + 操作成本　；参考：《物流技术》2014年01期

【摘要】：为了有效地利用物流成本估计中线性和非线性数学模型的优点,把线性预测性能优异的ARIMA数学模型和RBF神经网络相结合,使模型非线性数学变化上形成估计优化,可以捕捉物流成本价格的线性和非线性规律,有效地减少传统预测数学模型中一些非线性因素的影响。以某物流公司1991～2012年物流操作成本为数据,将所提出的数学模型与网格搜索SVR模型、PSO-SVR模型、Levenberg-Marquardt BP神经网络模型及背景值优化GM(1,1)模型进行对比实验。结果表明所提出的优化数学模型能够解决上述问题且具有更高的预测精度。
[Abstract]:In order to effectively utilize the advantages of linear and nonlinear mathematical models in logistics cost estimation, Arima mathematical model with excellent linear predictive performance is combined with RBF neural network to optimize the estimation of nonlinear mathematical variation of the model. It can capture the linear and nonlinear laws of logistics cost and price, and effectively reduce the influence of some nonlinear factors in the traditional predictive mathematical model. Taking the logistics operation cost of a logistics company from 1991 to 2012 as the data, this paper compares the proposed mathematical model with the grid search SVR model and the Levenberg-Marquardt BP neural network model and the background value optimization GM (1t1) model. The results show that the proposed optimal mathematical model can solve the above problems and has higher prediction accuracy.
【作者单位】：邢台职业技术学院;
【基金】：河北省社会科学基金项目“基于数学模型的物流配送成本优化体系构建”(HB12YJ006)
【分类号】：F253.7;F224

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