Neural Computing and Applications

, Volume 27, Issue 3, pp 707–725 | Cite as

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

  • Alireza Fallahpour
  • Ezutah Udoncy OluguEmail author
  • Siti Nurmaya Musa
  • Dariush Khezrimotlagh
  • Kuan Yew Wong
Original Article


Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.


Green supplier selection Data envelopment analysis (DEA) Artificial intelligence Genetic programming (GP) Parametric analysis 


  1. 1.
    Ahmady N, Azadi M, Sadeghi SAH, Saen RF (2013) A novel fuzzy data envelopment analysis model with double frontiers for supplier selection. Int J Logist Res Appl 16:87–98CrossRefGoogle Scholar
  2. 2.
    Alavi AH, Aminian P, Gandomi AH, Esmaeili MA (2011) Genetic-based modeling of uplift capacity of suction caissons. Expert Syst Appl 38:12608–12618CrossRefGoogle Scholar
  3. 3.
    Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28:242–274CrossRefzbMATHGoogle Scholar
  4. 4.
    Alavi AH, Mollahasani A, Gandomi AH, Bazaz JB (2012) Formulation of secant and reloading soil deformation moduli using multi expression programming. Eng Comput 29:173–197CrossRefGoogle Scholar
  5. 5.
    Awasthi A, Chauhan SS, Goyal S (2010) A fuzzy multicriteria approach for evaluating environmental performance of suppliers. Int J Prod Econ 126:370–378CrossRefGoogle Scholar
  6. 6.
    Azadeh A, Saberi M, Moghaddam RT, Javanmardi L (2011) An integrated data envelopment analysis–artificial neural network–rough set algorithm for assessment of personnel efficiency. Expert Syst Appl 38:1364–1373CrossRefGoogle Scholar
  7. 7.
    Azadeh A, Sheikhalishahi M, Asadzadeh S (2011) A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity. Renew Energy 36:3394–3401CrossRefGoogle Scholar
  8. 8.
    Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092CrossRefzbMATHGoogle Scholar
  9. 9.
    Basnet C, Leung JM (2005) Inventory lot-sizing with supplier selection. Comput Oper Res 32:1–14MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Bektas Ekici B, Aksoy UT (2011) Prediction of building energy needs in early stage of design by using ANFIS. Expert Syst Appl 38:5352–5358CrossRefGoogle Scholar
  11. 11.
    Bhasin M, Raghava G (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22:3195–3204CrossRefGoogle Scholar
  12. 12.
    Bhattacharya A, Geraghty J, Young P (2010) Supplier selection paradigm: an integrated hierarchical QFD methodology under multiple-criteria environment. Appl Soft Comput 10:1013–1027CrossRefGoogle Scholar
  13. 13.
    Boiral O (2006) Global warming: should companies adopt a proactive strategy? Long Range Plan 39:315–330CrossRefGoogle Scholar
  14. 14.
    Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36:11363–11368CrossRefGoogle Scholar
  15. 15.
    Büyüközkan G, Çifçi G (2011) A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Comput Ind 62:164–174CrossRefGoogle Scholar
  16. 16.
    Büyüközkan G, Çifçi G (2012) Evaluation of the green supply chain management practices: a fuzzy ANP approach. Prod Plan Control 23:405–418CrossRefGoogle Scholar
  17. 17.
    Büyüközkan G, Çifçi G (2012) A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Syst Appl 39:3000–3011CrossRefGoogle Scholar
  18. 18.
    Çelebi D, Bayraktar D (2008) An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Syst Appl 35:1698–1710CrossRefGoogle Scholar
  19. 19.
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Chen C, Tseng M, Lin Y, Lin Z (2010) Implementation of green supply chain management in uncertainty. In: 2010 IEEE international conference on industrial engineering and engineering management (IEEM). IEEE, pp 260–264Google Scholar
  21. 21.
    Chen SH, Hsieh CH (1999) Optimization of fuzzy simple inventory models. In: 1999 IEEE international fuzzy systems conference proceedings, 1999 FUZZ-IEEE’99. IEEE, pp 240–244Google Scholar
  22. 22.
    Chiou C, Hsu C, Hwang W (2008) Comparative investigation on green supplier selection of the American, Japanese and Taiwanese electronics industry in China. In: IEEE international conference on industrial engineering and engineering management, 2008, IEEM 2008. IEEE, pp 1909–1914Google Scholar
  23. 23.
    Dickson GW (1966) An analysis of vendor selection systems and decisions. J Purch 2:5–17Google Scholar
  24. 24.
    Emrouznejad A, Shale E (2009) A combined neural network and DEA for measuring efficiency of large scale datasets. Comput Ind Eng 56:249–254CrossRefGoogle Scholar
  25. 25.
    Esfahanipour A, Mousavi S (2011) A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Syst Appl 38:8438–8445CrossRefGoogle Scholar
  26. 26.
    Fan C, Wang X (2012) Supplier evaluation based on synthesis method of principal component analysis, data envelopment analysis and analytic hierarchy process. J Tongji Univ Nat Sci 40:1899–1904MathSciNetzbMATHGoogle Scholar
  27. 27.
    Fayaed SS, El-Shafie A, Jaafar O (2013) Adaptive neuro-fuzzy inference system-based model for elevation–surface area–storage interrelationships. Neural Comput Appl 22:987–998CrossRefGoogle Scholar
  28. 28.
    Feyzioğlu O, Büyüközkan G (2010) Evaluation of green suppliers considering decision criteria dependencies. In Multiple criteria decision making for sustainable energy and transportation systems. Springer, Berlin, pp 145–154Google Scholar
  29. 29.
    Forker LB, Mendez D (2001) An analytical method for benchmarking best peer suppliers. Int J Oper Prod Manag 21:195–209CrossRefGoogle Scholar
  30. 30.
    Gandomi AH, Alavi AH, Arjmandi P, Aghaeifar A, Seyednoor M (2010) Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders. J Mech Mater Struct 5:735–753CrossRefGoogle Scholar
  31. 31.
    Gandomi AH, Alavi AH, Mirzahosseini MR, Nejad FM (2010) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civ Eng 23:248–263CrossRefGoogle Scholar
  32. 32.
    Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion prediction equations. Eng Appl Artif Intell 24:717–732CrossRefGoogle Scholar
  33. 33.
    García-Arnau M, Manrique D, Rios J, Rodríguez-Patón A (2007) Initialization method for grammar-guided genetic programming. Knowl-Based Syst 20:127–133CrossRefGoogle Scholar
  34. 34.
    Ghodsypour SH, O’brien C (2001) The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. Int J Prod Econ 73:15–27CrossRefGoogle Scholar
  35. 35.
    González ME, Quesada G, Monge CAM (2004) Determining the importance of the supplier selection process in manufacturing: a case study. Int J Phys Distrib Logist Manag 34:492–504CrossRefGoogle Scholar
  36. 36.
    Govindan K, Rajendran S, Sarkis J, Murugesan P (2013) Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J Clean Prod. doi: 10.1016/j.jclepro.2013.06.046
  37. 37.
    Grisi RM, Guerra L, Naviglio G (2010) Supplier performance evaluation for green supply chain management. In: Paolo T (ed) Business performance measurement and management. Springer, Berlin, pp 149–163Google Scholar
  38. 38.
    Guneri AF, Yucel A, Ayyildiz G (2009) An integrated fuzzy-LP approach for a supplier selection problem in supply chain management. Expert Syst Appl 36:9223–9228CrossRefGoogle Scholar
  39. 39.
    Güneri AF, Ertay T, YüCel A (2011) An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Syst Appl 38:14907–14917CrossRefGoogle Scholar
  40. 40.
    Handfield R, Walton SV, Sroufe R, Melnyk SA (2002) Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process. Eur J Oper Res 141:70–87CrossRefzbMATHGoogle Scholar
  41. 41.
    Ho S-Y, Lee K-C, Chen S-S, Ho S-J (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42:1441–1446CrossRefGoogle Scholar
  42. 42.
    Ho W, Xu X, Dey PK (2010) Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur J Oper Res 202:16–24CrossRefzbMATHGoogle Scholar
  43. 43.
    Hong-jun L, Bin L (2010) A research on supplier assessment indices system of green purchasing. In: 2010 international conference on E-Business and E-Government (ICEE). IEEE, pp 3335–3338Google Scholar
  44. 44.
    Hong GH, Park SC, Jang DS, Rho HM (2005) An effective supplier selection method for constructing a competitive supply-relationship. Expert Syst Appl 28:629–639CrossRefGoogle Scholar
  45. 45.
    Hossein Alavi A, Hossein Gandomi A, Mollahassani A, Akbar Heshmati A, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173:368–379CrossRefGoogle Scholar
  46. 46.
    Hsu C-W, Hu AH (2009) Applying hazardous substance management to supplier selection using analytic network process. J Clean Prod 17:255–264CrossRefGoogle Scholar
  47. 47.
    Hsu C-W, Kuo T-C, Chen S-H, Hu AH (2013) Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. J Clean Prod 56:164–172CrossRefGoogle Scholar
  48. 48.
    Humphreys P, McCloskey A, McIvor R, Maguire L, Glackin C (2006) Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process. Int J Prod Res 44:2379–2419CrossRefGoogle Scholar
  49. 49.
    Jabbour ABL, Jabbour CJ (2009) Are supplier selection criteria going green? Case studies of companies in Brazil. Ind Manag Data Syst 109:477–495CrossRefGoogle Scholar
  50. 50.
    Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  51. 51.
    Kannan D, Khodaverdi R, Olfat L, Jafarian A, Diabat A (2013) Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. J Clean Prod 47:355–367CrossRefGoogle Scholar
  52. 52.
    Kannan D, Jabbour ABLdS, Jabbour CJC (2014) Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. Eur J Oper Res 233:432–447MathSciNetCrossRefzbMATHGoogle Scholar
  53. 53.
    Karpak B, Kumcu E, Kasuganti RR (2001) Purchasing materials in the supply chain: managing a multi-objective task. Eur J Purch Supply Manag 7:209–216CrossRefGoogle Scholar
  54. 54.
    Kazemi N, Ehsani E, Jaber M (2010) An inventory model with backorders with fuzzy parameters and decision variables. Int J Approximate Reasoning 51:964–972MathSciNetCrossRefzbMATHGoogle Scholar
  55. 55.
    Khezrimotlagh D, Salleh S, Mohsenpour Z (2013) A new method for evaluating decision making units in DEA. J Oper Res Soc 65:694–707Google Scholar
  56. 56.
    Khezrimotlagh D, Salleh S, Mohsenpour Z (2013) A new robust mixed integer-valued model in DEA. Appl Math Model 37:9885–9897MathSciNetCrossRefGoogle Scholar
  57. 57.
    Koza JR (1992) Genetic programming: Vol. 1. On the programming of computers by means of natural selection. MIT Press, CambridgezbMATHGoogle Scholar
  58. 58.
    Kuo R, Wang Y, Tien F (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18:1161–1170CrossRefGoogle Scholar
  59. 59.
    Lee AH, Kang H-Y, Hsu C-F, Hung H-C (2009) A green supplier selection model for high-tech industry. Expert Syst Appl 36:7917–7927CrossRefGoogle Scholar
  60. 60.
    Li X, Zhao C (2009) Selection of suppliers of vehicle components based on green supply chain. In: 16th international conference on industrial engineering and engineering management, 2009, IE&EM’09. IEEE, pp 1588–1591Google Scholar
  61. 61.
    Liao C-N, Kao H-P (2011) An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst Appl 38:10803–10811CrossRefGoogle Scholar
  62. 62.
    Link J, Yager P, Anjos J, Bediaga I, Castromonte C, Göbel C, Machado A, Magnin J, Massafferri A, de Miranda J (2005) Application of genetic programming to high energy physics event selection. Nucl Instrum Methods Phys Res Sect A 551:504–527CrossRefGoogle Scholar
  63. 63.
    Liou JJ, Chuang Y-C, Tzeng G-H (2013) A fuzzy integral-based model for supplier evaluation and improvement. Inf Sci 266:199–217MathSciNetCrossRefGoogle Scholar
  64. 64.
    Liu J, Ding F-Y, Lall V (2000) Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Manag Int J 5:143–150CrossRefGoogle Scholar
  65. 65.
    Mousavi S, Esfahanipour A, Zarandi MHF (2014) A novel approach to dynamic portfolio trading system using multitree genetic programming. Knowl Based Syst 66:68–81CrossRefGoogle Scholar
  66. 66.
    Mousavi SM, Alavi AH, Gandomi AH, Mollahasani A (2011) Nonlinear genetic-based simulation of soil shear strength parameters. J Earth Syst Sci 120:1001–1022CrossRefGoogle Scholar
  67. 67.
    Mousavi SM, Alavi AH, Mollahasani A, Gandomi AH (2011) A hybrid computational approach to formulate soil deformation moduli obtained from PLT. Eng Geol 123:324–332CrossRefGoogle Scholar
  68. 68.
    Mousavi SM, Aminian P, Gandomi AH, Alavi AH, Bolandi H (2012) A new predictive model for compressive strength of HPC using gene expression programming. Adv Eng Softw 45:105–114CrossRefGoogle Scholar
  69. 69.
    Muralidharan C, Anantharaman N, Deshmukh S (2002) A multi-criteria group decision making model for supplier rating. J Supply Chain Manag 38:22–33CrossRefGoogle Scholar
  70. 70.
    Narasimhan R, Talluri S, Mahapatra SK (2006) Multiproduct, multicriteria model for supplier selection with product life-cycle considerations. Decis Sci 37:577–603CrossRefGoogle Scholar
  71. 71.
    Nazari A (2013) Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials. Neural Comput Appl 23:417–427CrossRefGoogle Scholar
  72. 72.
    Noci G (1997) Designing ‘green’ vendor rating systems for the assessment of a supplier’s environmental performance. Eur J Purch Supply Manag 3:103–114CrossRefGoogle Scholar
  73. 73.
    Nourbakhsh V, Ahmadi A, Mahootchi M (2013) Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks. Int J Ind Eng Comput 4:273–284Google Scholar
  74. 74.
    Ozcan YA (2008) Health care benchmarking and performance evaluation: an assessment using Data Envelopment Analysis (DEA). Springer, BerlinCrossRefzbMATHGoogle Scholar
  75. 75.
    Pani MR, Verma R, Sahoo G (2012) A heuristic method for supplier selection using AHP, entropy and TOPSIS. Int J Procure Manag 5:784–796CrossRefGoogle Scholar
  76. 76.
    Rezaie K, Dalfard VM, Hatami-Shirkouhi L, Nazari-Shirkouhi S (2013) Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange. Neural Comput Appl 23:1–17CrossRefGoogle Scholar
  77. 77.
    Sanayei A, Farid Mousavi S, Yazdankhah A (2010) Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Syst Appl 37:24–30CrossRefGoogle Scholar
  78. 78.
    Santin D (2008) On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques. Appl Econ Lett 15:597–600CrossRefGoogle Scholar
  79. 79.
    Shavandi H, Ramyani SS (2013) A linear genetic programming approach for the prediction of solar global radiation. Neural Comput Appl 23:1197–1204CrossRefGoogle Scholar
  80. 80.
    Shekarian E, Gholizadeh AA (2013) Application of adaptive network based fuzzy inference system method in economic welfare. Knowl-Based Syst 39:151–158CrossRefGoogle Scholar
  81. 81.
    Shemshadi A, Shirazi H, Toreihi M, Tarokh MJ (2011) A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Syst Appl 38:12160–12167CrossRefGoogle Scholar
  82. 82.
    Shen L, Olfat L, Govindan K, Khodaverdi R, Diabat A (2013) A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resour Conserv Recycl 74:170–179CrossRefGoogle Scholar
  83. 83.
    Shi C, Bian D, Li S (2010) Application of BP neural network and DEA in the logistics supplier selection. In: 2010 2nd international conference on computer engineering and technology (ICCET). IEEE, pp V1-361–V361-364Google Scholar
  84. 84.
    Soltanifar M, Shahghobadi S (2013) Survey on rank preservation and rank reversal in data envelopment analysis. Knowl Based Syst 60:10–19CrossRefGoogle Scholar
  85. 85.
    Talluri S, Sarkis J (2002) A model for performance monitoring of suppliers. Int J Prod Res 40:4257–4269CrossRefzbMATHGoogle Scholar
  86. 86.
    Talluri S, Narasimhan R (2003) Vendor evaluation with performance variability: a max–min approach. Eur J Oper Res 146:543–552MathSciNetCrossRefzbMATHGoogle Scholar
  87. 87.
    Talluri S, Vickery SK, Narayanan S (2008) Optimization models for buyer–supplier negotiations. Int J Phys Distrib Logist Manag 38:551–561CrossRefGoogle Scholar
  88. 88.
    Vahdani B, Zandieh M (2010) Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method. Int J Prod Res 48:5307–5326CrossRefzbMATHGoogle Scholar
  89. 89.
    Vahdani B, Iranmanesh S, Mousavi SM, Abdollahzade M (2012) A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Appl Math Model 36:4714–4727MathSciNetCrossRefzbMATHGoogle Scholar
  90. 90.
    Weber CA, Ellram LM (1993) Supplier selection using multi-objective programming: a decision support system approach. Int J Phys Distrib Logist Manag 23:3–14CrossRefGoogle Scholar
  91. 91.
    Wei S, Zhang J, Li Z (1997) A supplier-selecting system using a neural network. In: 1997 IEEE international conference on intelligent processing systems, 1997, ICIPS’97. IEEE, pp 468–471Google Scholar
  92. 92.
    Wen U, Chi J (2010) Developing green supplier selection procedure: a DEA approach. In: 2010 IEEE 17th international conference on industrial engineering and engineering management (IE&EM). IEEE, pp 70–74Google Scholar
  93. 93.
    Wu D (2009) Supplier selection: a hybrid model using DEA, decision tree and neural network. Expert Syst Appl 36:9105–9112CrossRefGoogle Scholar
  94. 94.
    Wu J-D, Hsu C-C, Chen H-C (2009) An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference. Expert Syst Appl 36:7809–7817CrossRefGoogle Scholar
  95. 95.
    Xia W, Wu Z (2007) Supplier selection with multiple criteria in volume discount environments. Omega 35:494–504CrossRefGoogle Scholar
  96. 96.
    Yahya S, Kingsman B (1999) Vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method. J Oper Res Soc 50:916–930CrossRefzbMATHGoogle Scholar
  97. 97.
    Yan G (2009) Research on green suppliers’ evaluation based on AHP and genetic algorithm. In: 2009 international conference on signal processing systems. IEEE, pp 615–619Google Scholar
  98. 98.
    Yeh W-C, Chuang M-C (2011) Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst Appl 38:4244–4253CrossRefGoogle Scholar
  99. 99.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353MathSciNetCrossRefzbMATHGoogle Scholar
  100. 100.
    Zhang H, Li J, Merchant M (2003) Using fuzzy multi-agent decision-making in environmentally conscious supplier management. CIRP Ann Manuf Technol 52:385–388CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Alireza Fallahpour
    • 1
  • Ezutah Udoncy Olugu
    • 1
    Email author
  • Siti Nurmaya Musa
    • 1
  • Dariush Khezrimotlagh
    • 2
  • Kuan Yew Wong
    • 3
  1. 1.Department of Mechanical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Applied Statistics, Faculty of Economics and Administration BuildingUniversity of MalayaKuala LumpurMalaysia
  3. 3.Department of Manufacturing and Industrial Engineering, Faculty of Mechanical EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

Personalised recommendations