A Novel Hybrid Model for Vendor Selection in a Supply Chain by Using Artificial Intelligence Techniques Case Study: Petroleum Companies

Abstract

Oil is an important strategic material which is associated with vital and major components of national security and economy of each country. In this context, supplier selection in a supply chain of oil companies has a direct effect on immunization and optimization of the production cycle, refining and distribution of petroleum, gas and petroleum products in oil producer and exporter countries. Therefore, creating and owning a purposeful and intelligent process for evaluation and analysis of suppliers is one of the inevitable needs and concerns of these countries. Many of the methods which are currently widely applied in the management of oil companies utilize traditional supplier selection methods which are unfortunately limited to individual and subjective evaluation in weighing decision maker’s criteria, incorrect assessment rules, and inefficient decision-making methods. In this paper, with an in-depth look at the supplier selection in supply chain management of oil companies project, a novel model has been proposed based on an object-oriented framework. This model which finally leads to optimal selection and ranking of suppliers, reducing the time and cost in the selection process and also reduced human errors by using data mining techniques and neural networks in the reasoning method cycle based on the case. The proposed model was implemented on data bank information of the Oil Company. Finally, the results of the proposed model are compared with several other models. Results show that using reduced errors, improved accuracy, and efficiency the proposed model has been able to have a good performance in the supplier selection.