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A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP)

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Abstract

Supplier evaluation and selection is a complicated process which deals with conflicting attributes such as quality, cost. To mitigate the computational complexity, intelligent-based techniques have gained much popularity. But the main shortcoming of the existing models in this regard is to be a black box system. In this paper, we aim to combine analytical hierarchy process with multi-expression programming to both introduce a new evolutionary approach in the field of supplier evaluation and selection and cope with the earlier problem. To show the validity of the model, statistical test was carried out. The finding showed that the proposed model is accurate and acceptable for using in the evaluation process.

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References

  1. Fallahpour A, Moghassem A (2012) Evaluating applicability of VIKOR method of multi-criteria decision making for parameters selection problem in rotor spinning. Fibers Polym 13:802–808

    Article  Google Scholar 

  2. Kuo R, Hsu C, Chen Y  (2015) Integration of fuzzy ANP and fuzzy TOPSIS for evaluating carbon performance of suppliers. Int J Environ Sci Technol 1–14. doi:10.1007/s13762-015-0819-9

  3. Vahdani B, Iranmanesh S, Mousavi SM, Abdollahzade M (2012) A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Appl Math Model 36:4714–4727

    Article  MathSciNet  MATH  Google Scholar 

  4. Fallahpour A, Olugu EU, Musa SN, Khezrimotlagh D, Wong KY (2015) An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Comput Appl 1–19. doi:10.1007/s00521-015-1890-3

  5. Golmohammadi D (2011) Neural network application for fuzzy multi-criteria decision making problems. Int J Prod Econ 131:490–504

    Article  Google Scholar 

  6. Güneri AF, Ertay T, YüCel A (2011) An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Syst Appl 38:14907–14917

    Article  Google Scholar 

  7. Golmohammadi D, Creese RC, Valian H, Kolassa J (2009) Supplier selection based on a neural network model using genetic algorithm. IEEE Trans Neural Netw 20:1504–1519

    Article  Google Scholar 

  8. Azadeh A, Saberi M, Anvari M (2011) An integrated artificial neural network fuzzy C-means-normalization algorithm for performance assessment of decision-making units: the cases of auto industry and power plant. Comput Ind Eng 60:328–340

    Article  Google Scholar 

  9. Kuo R, Hong S, Huang Y (2010) Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Appl Math Model 34:3976–3990

    Article  MATH  Google Scholar 

  10. Özkan G, İnal M (2014) Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems. Appl Soft Comput 24:232–238

    Article  Google Scholar 

  11. Fallahpour A, Olugu EU, Musa SN, Khezrimotlagh D, Singh S (2014) Supplier selection under fuzzy environment: a hybrid model using KAM in DEA. In: Emrouznejad A, Banker R, Doraisamy SM, Arabi B (eds) Recent developments in data envelopment analysis and its applications, pp 342–348

  12. Oztaysi B (2014) A decision model for information technology selection using AHP integrated TOPSIS-Grey: the case of content management systems. Knowl Based Syst 70:44–54

    Article  Google Scholar 

  13. Deng X, Hu Y, Deng Y, Mahadevan S (2014) Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl 41:156–167

    Article  Google Scholar 

  14. Mikhailov L, Tsvetinov P (2004) Evaluation of services using a fuzzy analytic hierarchy process. Appl Soft Comput 5:23–33

    Article  Google Scholar 

  15. Oltean M, Dumitrescu D (2002) Multi expression programming, unpublished. http://www.mep.cs.ubbcluj.ro/papers.htm

  16. Hossein A, Alavi A, Mollahasani A, Hossein Gandomi J, Boluori Bazaz J (2012) Formulation of secant and reloading soil deformation moduli using multi expression programming. Eng Comput 29:173–197

    Article  Google Scholar 

  17. Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comput 26:111–118

    Article  Google Scholar 

  18. Çelebi D, Bayraktar D (2008) An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Syst Appl 35:1698–1710

    Article  Google Scholar 

  19. Kuo RJ, Wang YC, Tien FC (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18:1161–1170

    Article  Google Scholar 

  20. Lima FR, Junior L, Osiro LCR Carpinetti (2013) A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Appl Soft Comput 13:4133–4147

    Article  Google Scholar 

  21. Emrouznejad A, Shale E (2009) A combined neural network and DEA for measuring efficiency of large scale datasets. Comput Ind Eng 56:249–254

    Article  Google Scholar 

  22. Smith GN (1986) Probability and statistics in civil engineering: an introduction. Collins, London

    Google Scholar 

  23. Mostafavi ES, Mostafavi SI, Jaafari A, Hosseinpour F (2013) A novel machine learning approach for estimation of electricity demand: an empirical evidence from Thailand. Energy Convers Manag 74:548–555

    Article  Google Scholar 

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Acknowledgments

This work is sponsored by Malaysian Ministry of Higher Education High Impact Research (HIR-MOHE) Project (UM.C/HIR/MOHE/ENG/01) and the University of Malaya Research Grant Project (RP018C-13AET).

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Correspondence to Ezutah Udoncy Olugu.

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Fallahpour, A., Olugu, E.U. & Musa, S.N. A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Comput & Applic 28, 499–504 (2017). https://doi.org/10.1007/s00521-015-2078-6

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  • DOI: https://doi.org/10.1007/s00521-015-2078-6

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