An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain. (January 2020)

• Record Type: Journal Article
• Authors: Cheng, Yijun; Peng, Jun; Gu, Xin; Zhang, Xiaoyong; Liu, Weirong; Zhou, Zhuofu; Yang, Yingze; Huang, Zhiwu
• Is Part Of: Computers & industrial engineering. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Supply chain management -- Supplier evaluation -- Support vector regression -- Multiple criteria decision making -- Genetic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.04.047
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: An intelligent supplier evaluation model is proposed to alleviate the pressure on experts involved in evaluation process. An integrated MCDM is adopted to label each supplier by analyzing data in three aspects. Three critical parameters of the SVR model are evolved through genetic programming without prior knowledge. The accuracy and robustness of the proposed intelligent model is verified though ARCIC data set. Abstract: Supplier evaluation is an important issue in supply chain management. Most existing studies rely on expert experience to evaluate supplier performance. In order to alleviate the pressure on experts in global supply chain, an intelligent supplier evaluation model based on data-driven support vector regression (SVR) is proposed in this paper. Two methods are used in the construction process of the proposed intelligent model for supplier evaluation. The integrated multiple criteria decision making (MCDM) is employed to obtain the label of each supplier instead of the manual label. Then the obtained labels are used to train the SVR. Genetic programming (GP) is adopted to set three critical parameters of SVR without prior knowledge, which are kernel function k ( · ), the penalty parameter C, and the tolerable deviation ε . The performance of the proposed intelligent model is evaluated with the commercially available ARCIC data set. Simulation results show that the accuracy and robustness of proposed intelligent model are superior when compared with existing models.