基于蚁群算法和神经网络的物流配送中心选址决策

Location Decision on Distribution Center Selection Based on Ant Colony Optimization and Neural Network

将蚁群聚类算法和径向基神经网络进行融合,提出了ACC-RBF算法,弥补两种算法各自存在的不足之处,并将其用于物流配送中心的选址问题,结合实验数据进行训练得出最终评价模型;实验证明,与传统的物流选址决策方法相比,该模型能有效、快速地解决物流配送中心的选址问题。

To combine ant colony clastering optimization with radial basis function neural network, raised ACC-RBF algorithm to make up the shortages existing in each of the two algorithms and to make use of it in the matter of location selection of logistics distribution center, combining experimental data to carry out training to obtain final evaluation model; experimental results show, compared with traditional logistics location selection decision method, the model can effectively and quickly solve the matter of logistics distribution center location selection.