The Reverse Logistics Evaluation Based on Kpca-Linmap Model

According to the limitation of principal components analysis (PCA) in dealing with the nonlinear data, connecting with the linear programming techniques for multidimensional analysis of preference (LINMAP), this paper presents the kernel principal components analysis-linear programming techniques for multidimensional analysis of preference (KPCA-LINMAP) evaluation model. In addition, the weight of each index can be obtained in this model, thus it makes up another shortage of PCA. In reverse logistics evaluation, the indexes are numerous and the degree of correlation is not high, the model is fitter for this situation than the traditional PCA. At last, the validity and the advantage of this method are verified by an instance