基于支持向量机的物流配送中心选址决策

建立了选址决策的模糊评价矩阵,应用支持向量机方法(SVM)来处理数据,进行物流配送中心的选址决策。支持向量回归机根据所提供的数据,通过学习和训练,找出输入与输出的内在联系,从而求取问题的解,而不是根据经验知识,因而具有自适应功能,能弱化指标权重确定中人为因素的影响。与传统方法相比较,有较好的泛化能力,能较客观地对多个选址方案的优劣进行评价。最后,引用实例说明利用支持向量回归机完成评价工作的全部步骤。

Distribution Centers Site Selection Based on Support Vector Machine

A new distribution centers site selection method is proposed,using support vector machine(SVM),where the data derived from a fuzzy appraisal matrix is processed.By learning and training,we use the data of this subset to get the solution and find interrelationship of input and output by the support vector regression machine.This method has the advantage of self adaptability to weaken the human factors in fixing weight index.The new method has better extensive capability than traditional methods.It can make the site selection in an objective manner and avoid the bias brought up by traditional methods.Practical examples are cited in this paper to illustrate the process.