The green supplier selection method for chemical industry with analytic network process and radial basis function neural network

Green supplier selection in chemical industry is an important issue especially in the era of lowcarbon economy. In this paper, a supplier selection method for chemical industry is proposed based on analytic network process (ANP) and radial basis function (RBF) neural network and on the philosophy of green supply chain management (GSCM). We first put forward several distinctive criteria which contain not only traditional supplier selection factors but also environmental factors. We then apply them in ANP to derive the weights of the criteria. With all the weights of the criteria, we incorporate the RBF neural network into alternatives selection process. During RBF neural network training procedure, implied knowledge is extracted from the training data and can be conveniently used in a new supplier selection process. Therefore, the method possesses dynamic assessment capability. Finally, a numerical example is given to illustrate the application of the two-stage integrated model. The results show that the proposed method has the feasibility and effectiveness for green supplier selection in chemical industry.