The Reverse Logistics Evaluation Based on Kpca-Linmap Model

Author

Zhang, Cai-qing ; Lu, Yan-chao

Author_Institution

Dept. of Econ. Manage., North China Electr. Power Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2531

Lastpage

2535

Abstract

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

Keywords

linear programming; principal component analysis; reverse logistics; KPCA-LINMAP model; kernel principal components analysis; linear programming techniques; multidimensional preference analysis; reverse logistics; Costs; Cybernetics; Environmental economics; Joining processes; Kernel; Linear programming; Machine learning; Multidimensional systems; Power generation economics; Principal component analysis; Reverse logistics; Support vector machines; Kernel Function; Principal Components Analysis; Reverse Logistics Evaluation; the Coupling Model of LINMAP;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258844

Filename

4028490