Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking
نویسندگان
چکیده
Supplier selection problem (SSP) is a problem to select the best among suppliers based on input and output data of the suppliers. Since different uncontrollable and unpredictable parameters are affecting selection, choosing the best supplier is a complicated process. Data Envelopment Analysis (DEA) is a method for measuring efficiency and inefficiencies of Decision Making Units (DMUs). While, the DEA has been employed by many researchers but it still has disadvantages. On the other hand, it has been widely used in SSP with inputs for supplier evaluation. Therefore, it seems need to provide models for further discrimination among these suppliers. Hence, in this paper, a combination of DEA and Neural Networks (NNs) model, DEA-NNs, has been improved based on Back-Propagation (BP) algorithm of NNs for complete ranking and prediction of supplier selection and performance.
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