Nonlinear genetic-based model for supplier selection: a comparative study

Abstract

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.

Keywords:

• Gene Expression Programming (GEP)
• Artificial Neural Network (ANN)
• Data Envelopment Analysis (DEA)
• supplier selection

JEL Classification:

• C44
• C61

Additional information

Notes on contributors

Alireza Fallahpour

Alireza FALLAHPOUR is Master, received from Islamic Azad University of Iran in 2010. Now he is research assistant at University of Malaya (UM) and is studying for his doctorate of mechanical engineering at UM, Malaysia. Research interests: supply chain management, intelligent approaches and multiple criteria decision making.

Atefeh Amindoust

Atefeh AMINDOUST is Doctor at the Islamic Azad University, Najafabad branch, Iran. Her research interests include supply chain management, decision making and fuzzy logic.

Jurgita Antuchevičienė

Jurgita ANTUCHEVIČIENĖ is Doctor, Professor at the Department of Construction Technology and Management, Vilnius Gediminas Technical University, Vilnius, Lithuania. She is a member of EURO Working Groups Multicriteria Decision Aiding and OR in Sustainable Development and Civil Engineering, Editorial Board member of three international research journals. Her research interests include sustainable development, construction business management and investment, multiple criteria analysis, decision-making theories and decision support systems.

Morteza Yazdani

Morteza YAZDANI, is a PhD scholar in business and economic at Universidad Europea de Madrid, Spain. His main research interest is application of multi criteria decision making in different fields of knowledge as material science, supply chain management and also strategic planning. He has published papers in some international journals as International Journal of Strategic Decision Science, Expert Systems with Applications, Materials & Design, Journal of Civil engineering and management, International Journal of Business and Systems Research and International Journal of Logistics Research.