Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks

Journal of Cleaner Production

Volumn 142, Issue , 2017, Pages 1098-1107

  Shabanpour, Hadi ^a   Yousefi, Saeed ^a   Saen, Reza Farzipoor ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Artificial neural networks; Dynamic data envelopment analysis; Forecasting of future efficiency; Green supplier selection

Indexed keywords

DATA ENVELOPMENT ANALYSIS; EFFICIENCY; FORECASTING; NEURAL NETWORKS;

DECISION MAKERS; DECISION MAKING UNIT; DYNAMIC DATA; FORECASTING EFFICIENCIES; FUTURE PLANNING; GREEN SUPPLIER; GREEN SUPPLIER SELECTIONS; TRADITIONAL MODELS;

DECISION MAKING;

EID: 84994876267     PISSN: 09596526     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jclepro.2016.08.147     Document Type: Article

References (49)

1. Allen, E., Seaman, C., Likert scales and data analyses. Qual. Prog. 40:7 (2007), 64–65.
2. Andersen, P., Petersen, N.C., A procedure for ranking efficient units in data envelopment analysis. Manage. Sci. 39:10 (1993), 1261–1264.
3. Azadi, M., Farzipoor Saen, R., Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs. Int. J. Oper. Res. 13:1 (2012), 44–66.
4. Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multi criteria approach for evaluating environmental performance of suppliers. Int. J. Prod. Econ. 126:2 (2010), 370–378.
5. Berkel, R.V., Cleaner production and eco-efficiency initiatives in Western Australia 1996-2004. J. Clean. Prod. 15:8–9 (2007), 741–755.
6. Büyüközkan, G., Çifçi, G., A novel fuzzy multi criteria decision framework for sustainable supplier selection with incomplete information. Comput. Ind. 62:2 (2011), 164–174.
7. Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2:6 (1978), 429–444.
8. Chun Tsai, M., Ping Lin, S., Chan Cheng, C., Ping Lin, Y., The consumer loan default predicting model-An application of DEA-DA and neural network. Expert Syst. Appl. 36:4 (2009), 11682–11690.
9. Desheng, W.U., Yang, Z., Liang, L., Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Syst. Appl. 31:1 (2006), 108–115.
10. Emrouznejad, A., Shale, E., A combined neural network and DEA for measuring efficiency of large scale datasets. Comput. Ind. Eng. 56:1 (2009), 249–254.
11. Färe, R., Grosskopf, S., Inter-temporal Production Frontiers: with Dynamic DEA. 1996, Springer.
12. Farrell, M.J., The measurement of productive efficiency. J. R. Stat. Soc. 120:3 (1957), 253–281.
13. Farzipoor Saen, R., Using super-efficiency analysis for ranking suppliers in the presence of volume discount offers. Int. J. Phys. Distrib. Logist. Manage 38:8 (2008), 637–651.
14. Farzipoor Saen, R., Using cluster analysis and DEA-discriminant analysis to predict group membership of new customers. Int. J. Bus. Exc 6:3 (2013), 348–360.
15. Gevrey, M., Worner, S.P., Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method. J. Econ. Entomol. 99:3 (2006), 979–986.
16. Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J. Clean. Prod. 47 (2013), 345–354.
17. Hagan, M.T., Menhaj, M., Training feed forward networks with the Marquardt algorithm. IEEE Tran. Neur.Net 5:6 (1994), 989–993.
18. Ibiwoye, A., Ajibola, E., Sogunro, A.B., Artificial neural network model for predicting insurance insolvency. Int. J. Manag. Bus. Res. 2:1 (2012), 59–68.
19. Krakovsky, R., Forgac, R., Neural network approach to multidimensional data classification via clustering. IEEE. 9th International Symposium on Intelligent Systems and Informatics, 2011, 169–174.
20. Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection. J. Clean. Prod. 18:12 (2010), 1161–1170.
21. Lee, H.S., Zhu, J., Super efficiency infeasibility and zero data in DEA. Eur. J. Oper. Res. 216:3 (2012), 429–433.
22. Liao, C.N., Kao, H.P., An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst. Appl. 38:9 (2011), 10803–10811.
23. Likert, R., A Technique for the measurement of attitudes. Arch. Psy 22:140 (1932), 1–55.
24. Lu, P., Rosenbaum, M.S., Artificial neural network and grey system for the prediction of slope stability. Nat. Hazards 30:3 (2003), 383–398.
25. Mahdiloo, M., Farzipoor Saen, R., Lee, K.H., Technical, environmental and eco-efficiency measurement for supplier selection: an extension and application of data envelopment analysis. Int. J. Prod. Econ. 168 (2015), 279–289.
26. McCulloch, W., Pitts, W., A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5:4 (1943), 115–133.
27. Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorpho 94:3 (2008), 379–400.
28. Nguyen, H.G., Using neutral work in predicting corporate failure. J. Soc. Sci. 1:4 (2005), 199–202.
29. Noorizadeh, A., Mahdiloo, M., Farzipoor Saen, R., Supplier selection in the presence of dual role factors, nondiscretionary inputs, and weight restrictions. Int. J. Product. Qual. Manage 8:2 (2011), 134–152.
30. Noorizadeh, A., Mahdiloo, M., Farzipoor Saen, R., Evaluating relative value of customers via data envelopment analysis. J. Bus. Ind. Mar. 27:7 (2013), 577–588.
31. Rashidi, K., Shabani, A., Frazipoor Saen, R., Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the OECD countries. J. Clean. Prod. 105 (2015), 241–252.
32. Ribeiro, F.M., Kruglianskas, L., Improving environmental permitting through performance based regulation: a case study of Sao Paulo State. J. Clean. Prod. 46 (2013), 15–26.
33. Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back propagating errors. Nature 323:6088 (1986), 533–536.
34. Sengupta, J.K., Dynamics of Data Envelopment Analysis. 1995, Klu. Acad. Pub, Dordrecht.
35. Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: critique and extensions. Silkman, R.H., (eds.) Measuring Efficiency: an Assessment of Data Envelopment Analysis, 1986, Jossey-Bass, San Francisco 32(1), 73–105.
36. Shabani, A., Farzipoor Saen, R., Torabipour, S.M.R., A new benchmarking approach in cold chain. Appl. Math. Modell. 36:1 (2012), 212–224.
37. Stewart, T.J., Goal directed benchmarking for organizational efficiency. Omega 38:6 (2010), 534–539.
38. Tavana, M., Shabanpour, H., Yousefi, S., Farzipoor Saen, R., A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation. Neural comput. Appl., 2016, 10.1007/s00521-016-2274-z (in press).
39. Tone, K., Tsutsui, M., Dynamic DEA: a slacks-based measure approach. Omega 38:3–4 (2010), 145–156.
40. Tone, K., Tsutsui, M., Dynamic DEA with network structure: a slacks-based measure approach. Omega 42:1 (2014), 124–131.
41. Wang, M.Y., Sang Chain, K., A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. Int. J. Prod. Res. 47:23 (2009), 6663–6679.
42. Wang, N.S., Yi, R.H., Wang, W., Evaluating the performances of decision-making units based on interval efficiencies. J. Comput. Appl. Math. 216:2 (2008), 328–343.
43. Yeh, W.C., Chuang, M.C., Using multi objective genetic algorithm for partner selection in green supply chain problems. Expert Syst. Appl. 38:4 (2011), 4244–4253.
44. Yousefi, S., Shabanpour, H., Fisher, R., Farzipoor Saen, R., Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis. Measurement 83 (2016), 72–85.
45. Yousefi, S., Shabanpour, H., Farzipoor Saen, R., Faramarzi, G.R., Making an ideal decision making unit using virtual network data envelopment analysis approach. Int. J. Bus. Perform. Manage 15:4 (2014), 316–328.
46. Zhang, G.P., Neural networks for classification: a survey. IEEE Trans. Syst. Man. Cybern. C. Appl. Rev. 30:4 (2000), 451–462.
47. Zhang, W.J., Simulation of arthropod abundance from plant composition. Comput. Ecol. Softw. 1:1 (2011), 37–48.
48. Zhang, G., Hu, M.Y., Patuwo, E.B., Indro, D., Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur. J. Oper. Res. 116:1 (1999), 16–32.
49. Zhang, W.J., Wei, W., Spatial succession modeling of biological communities: a multi-model approach. Environ. Monit. Assess. 158:1–4 (2009), 213–230.