Resumen:
Evaluation and selection of candidate suppliers has become a major decision in business
activities around the world. In this paper, a new hybrid approach based on integration of Gene Expression
Programming (GEP) with Data Envelopment Analysis (DEA) (DEA-GEP) is presented to
overcome the supplier selection problem. First, suppliers’ efficiencies are obtained through applying
DEA. Then, the suppliers’ related data are utilized to train GEP to find the best trained DEA-GEP
algorithm for predicting efficiency score of Decision Making Units (DMUs). The aforementioned
data is also used to train Artificial Neural Network (ANN) to predict efficiency scores of DMUs.
The proposed hybrid DEA-GEP is compared to integrated approach of Artificial Neural Network
with Data Envelopment Analysis (DEA-ANN) to support the validity of the proposed model. The
obtained results clearly show that the model based on GEP not only is more accurate than the
DEA-ANN model, but also presents a mathematical function for efficiency score based on input and
output data set. Finally, a real-life supplier selection problem is presented to show the applicability
of the proposed hybrid DEA-GEP model.