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

AbstractEvaluation 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 efficien...

[1]  WuChih-Hung,et al.  Fuzzy DEMATEL method for developing supplier selection criteria , 2011 .

[2]  A. Amida,et al.  Fuzzy multiobjective linear model for supplier selection in a supply chain , 2015 .

[3]  P. K. Humphreysa,et al.  Integrating environmental criteria into the supplier selection process , 2015 .

[4]  Xiaowei Xu,et al.  Multi-criteria decision making approaches for supplier evaluation and selection: A literature review , 2010, Eur. J. Oper. Res..

[5]  Mohsen Shanbeh,et al.  An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming , 2012 .

[6]  Alireza Fallahpour,et al.  Processing parameters optimization of draw frame for rotor spun yarn strength using gene expression programming (GEP) , 2011 .

[7]  P. Sampaio,et al.  ISO 9001 certification research: questions, answers and approaches , 2009 .

[8]  Gary W. Dickson,et al.  AN ANALYSIS OF VENDOR SELECTION SYSTEMS AND DECISIONS , 1966 .

[9]  Stanislav Karapetrovic,et al.  ISO 10002 Complaints Handling System: a study , 2006 .

[10]  Paul Young,et al.  Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment , 2010, Appl. Soft Comput..

[11]  S. Meysam Mousavi,et al.  A locally linear neuro-fuzzy model for supplier selection in cosmetics industry , 2012 .

[12]  Atakan Yücel,et al.  An integrated fuzzy-lp approach for a supplier selection problem in supply chain management , 2009, Expert Syst. Appl..

[13]  Reza Farzipoor Saen,et al.  A new mathematical approach for suppliers selection: Accounting for non-homogeneity is important , 2007, Appl. Math. Comput..

[14]  Sevinç Ilhan Omurca,et al.  An intelligent supplier evaluation, selection and development system , 2013, Appl. Soft Comput..

[15]  Dariush Khezrimotlagh,et al.  An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach , 2015, Neural Computing and Applications.

[16]  Amir Hossein Gandomi,et al.  Permanent deformation analysis of asphalt mixtures using soft computing techniques , 2011, Expert Syst. Appl..

[17]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[18]  Elham Sadat Mostafavi,et al.  Gene expression programming as a basis for new generation of electricity demand prediction models , 2014, Comput. Ind. Eng..

[19]  Ali Danandeh Mehr,et al.  Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique , 2013 .

[20]  F. Liu,et al.  The voting analytic hierarchy process method for selecting supplier , 2005 .

[21]  Seyed Hassan Ghodsypour,et al.  A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming , 1998 .

[22]  Amir Hossein Alavi,et al.  A robust data mining approach for formulation of geotechnical engineering systems , 2011 .

[23]  Ezgi Aktar Demirtaş,et al.  An integrated multiobjective decision making process for supplier selection and order allocation , 2008 .

[24]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[25]  Amy Hsin-I Lee,et al.  A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks , 2009, Expert Syst. Appl..

[26]  Reza Farzipoor Saen,et al.  Suppliers selection in the presence of both cardinal and ordinal data , 2007, Eur. J. Oper. Res..

[27]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[28]  Morteza Yazdani,et al.  New integration of MCDM methods and QFD in the selection of green suppliers , 2016 .

[29]  James Nga-Kwok Liu,et al.  Application of decision-making techniques in supplier selection: A systematic review of literature , 2013, Expert Syst. Appl..

[30]  Majid Azadi,et al.  A novel network data envelopment analysis model for evaluating green supply chain management , 2014 .

[31]  Feyzan Arikan,et al.  A fuzzy solution approach for multi objective supplier selection , 2013, Expert Syst. Appl..

[32]  Weijun Xia,et al.  Supplier selection with multiple criteria in volume discount environments , 2007 .

[33]  Shaoliang Zhang,et al.  Parse-matrix evolution for symbolic regression , 2012, Eng. Appl. Artif. Intell..

[34]  Pratima Bansal,et al.  Strategic Explanations for the Early Adoption of ISO 14001 , 2003 .

[35]  Shouhong Wang,et al.  Adaptive non-parametric efficiency frontier analysis: a neural-network-based model , 2003, Comput. Oper. Res..

[36]  罗长童,et al.  Parse-matrix evolution for symbolic regression , 2012 .

[37]  Amir Hossein Alavi,et al.  Nonlinear modeling of soil deformation modulus through LGP-based interpretation of pressuremeter test results , 2012, Eng. Appl. Artif. Intell..

[38]  Abbas Ahmadi,et al.  Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks , 2013 .

[39]  R. J. Kuo,et al.  Integration of artificial neural network and MADA methods for green supplier selection , 2010 .

[40]  Dilay Çelebi,et al.  An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information , 2008, Expert Syst. Appl..

[41]  Dariush Khezrimotlagh,et al.  Supplier selection under fuzzy environment: A hybrid model using kam in DEA , 2014 .

[42]  Desheng Dash Wu,et al.  Supplier selection: A hybrid model using DEA, decision tree and neural network , 2009, Expert Syst. Appl..

[43]  Felix T.S. Chan,et al.  Integrating environmental criteria into the supplier selection process , 2003 .

[44]  Chen-Tung Chen,et al.  A fuzzy approach for supplier evaluation and selection in supply chain management , 2006 .

[45]  Li Zhao,et al.  Hoeffding bound based evolutionary algorithm for symbolic regression , 2012, Eng. Appl. Artif. Intell..

[46]  Ali Emrouznejad,et al.  A combined neural network and DEA for measuring efficiency of large scale datasets , 2009, Comput. Ind. Eng..

[47]  Amir Hossein Alavi,et al.  Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .

[48]  Daniel Santín.  On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques , 2004 .

[49]  Atakan Yücel,et al.  An approach based on ANFIS input selection and modeling for supplier selection problem , 2011, Expert Syst. Appl..

[50]  J. Rezaei.  A two-way approach to supply chain partner selection , 2015 .

[51]  Fong-Yuen Ding,et al.  Using data envelopment analysis to compare suppliers for supplier selection and performance improvement , 2000 .

[52]  Desheng Dash Wu,et al.  Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank , 2006, Expert Syst. Appl..

[53]  Gülçin Büyüközkan,et al.  A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information , 2011, Comput. Ind..

[54]  WangLei,et al.  Hoeffding bound based evolutionary algorithm for symbolic regression , 2012 .

[55]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[56]  G. Kannan,et al.  Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment , 2006 .

[57]  Amir Hossein Gandomi,et al.  Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems , 2012, Neural Computing and Applications.

[58]  Hamza Güllü,et al.  Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash , 2014, Eng. Appl. Artif. Intell..

[59]  Morteza Yazdani,et al.  An integrated fuzzy ANP–QFD approach for green building assessment , 2016 .

[60]  S. H. Ghodsypour,et al.  Fuzzy multiobjective linear model for supplier selection in a supply chain , 2006 .

[61]  Jinlong Zhang,et al.  A supplier-selecting system using a neural network , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[62]  Chih-Hung Wu,et al.  Fuzzy DEMATEL method for developing supplier selection criteria , 2011, Expert Syst. Appl..

[63]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[64]  Victor B. Kreng,et al.  Supplier management for manufacturer – a case study of flexible PCB , 2005 .

[65]  Amir Hossein Gandomi,et al.  Applications of Computational Intelligence in Behavior Simulation of Concrete Materials , 2011, Computational Optimization and Applications in Engineering and Industry.

[66]  Gül Tekin Temur,et al.  DEA ANN Approach in Supplier Evaluation System , 2009 .

[67]  Amir Hossein Gandomi,et al.  Genetic-based modeling of uplift capacity of suction caissons , 2011, Expert Syst. Appl..

[68]  A. Gandomi,et al.  Empirical modeling of plate load test moduli of soil via gene expression programming , 2011 .

[69]  Morteza Yazdani,et al.  A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS , 2015 .

[70]  Ardeshir Bahreininejad,et al.  Sustainable supplier selection: A ranking model based on fuzzy inference system , 2012, Appl. Soft Comput..

[71]  P. Bauer.  Recent developments in the econometric estimation of frontiers , 1990 .