RBF Neural Network for the Priority of Buyer Order in Supply Chain

Abstract

The priority of buyer order is a key issue in production scheduling in MTO (make to order) enterprises. In view of the deficiencies in current studies related to the assessment of the priority, a new emerging method for determining the priority in supply chain based on radial basis function (RBF) neural network is put forward which considers the constraint in supply chain and the complicated relation between the evaluation-index system and the priority. The evaluation-index system covers the constraints or determining factors not only in the enterprise and buyers but also in supply chain, and hence is a more complete and effective. A 3-layer RBF neural network model which takes form of the Gauss Function is built for determining the priority of buyer order. And finally by the testing and contradistinctive analysis of the three different methods, this study showed that the evaluation method ground on RBF neural network exceeds that on traditional AHP and BP neural network under supply chain environment, the assessment result is more accurate while the model training time is shorter using the RBF neural network in supply chain.