A case based reasoning approach on supplier selection in petroleum enterprises

Research highlights? We propose a new method based on case reasoning system (CBR) for supplier selection of petroleum enterprises. ? Based on decision-making tree and information entropy methods to evaluate the importance of multiattributes objectively. ? Based on k-prototypes clustering algorithm to evaluate multi semantic expression synthetically. ? Based on BP neural networks method to distill rules from the decision-making results. Petroleum is an important strategic material which is connected with the vitals and safety of the national economy, and the supplier selections are related to the safety of petroleum production and supply. However, the traditional approaches for supplier selections are limited in subjective evaluation of weights, inaccurate assessing rules, and inefficient decision-making. Although most of the current methods are widely applied in corporation management, a more efficient approach needs to be proposed for supplier selection of oil enterprise.This paper summarizes the particular characteristics of the supply chain of Chinese petroleum enterprises, analyzes the limitations of the traditional methods of supplier selection, and brought forward the method based on case reasoning system (CBR) for petroleum enterprises. The method based on data mining techniques which solves three key problems of CBR, includes calculating the weights of the attributes with information entropy in case warehouse organizing process objectively, evaluating the similarities with k-prototype clustering between the original and target cases in case retrieving process exactly, and extracting the potential rules with back propagation neural networks from conclusions in maintenance and revising process efficiently. It demonstrates the advantages, practicability and validity of this method via case study finally.