Determining the Customer Satisfaction in Automobile Sector Using the Intuitionistic Fuzzy Analytical Hierarchy Process

  • S. Rajaprakash
  • R. Ponnusamy
  • J. Pandurangan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)


Customer satisfaction is an important factor sustaining the business and its further development of the organization. To retain the customer is one of the important task in production industries. In these days of high competition customer satisfaction is very much essential, but uncertainty creeps. Analytical hierarchy process (AHP) is an important theory in the decision making problem. In this work we are combining Intuitionistic Fuzzy Analytical Process (IFAHP).The intuitionistic fuzzy set is able to give a very good outcome on uncertainty, and vagueness. Therefore the objective of the work is using Intuitionistic fuzzy analytical hierarchy process (IFAHP) to determine the customer satisfaction.


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  1. 1.
    Deschrijver, G., Cornelis, C., Kerre, E.: On the representation of intuitionistic fuzzy t-norms and t-conorms. Notes on Intuitionistic Fuzzy Sets 8(3), 1–10 (2002)MATHMathSciNetGoogle Scholar
  2. 2.
    Xu, Z.: Intuitionistic preference relations and their application in group decision making. Inf. Sci. 177(11), 2363–2379 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Saaty, T.: The Analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York (1980)MATHGoogle Scholar
  4. 4.
    Xu, Z., Liao, H.: Intuitionistic fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems 22(4), 749–761 (2014)CrossRefGoogle Scholar
  5. 5.
    Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst. 114(3), 505–518 (2000)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Abdullah, L., Jaafar, S., Imran: Intuitionistic fuzzy analytic hierarchy process approach in ranking of human capital indicator. Journal of Applied Science 3(1), 423–429 (2013)Google Scholar
  8. 8.
    Catak, F.O., Karabas, S., Yildirim, S.: Fuzzy analitical hierarchy based dbms selection in turikish national identity card management project. IJIST 4(2), 212–224 (2012)Google Scholar
  9. 9.
    Rajaprakash, S., Ponnusamy, R.: Determining students expectation in present education system using fuzzy analytic hierarchy process. In: Prasath, R., Kathirvalavakumar, T. (eds.) MIKE 2013. LNCS, vol. 8284, pp. 553–566. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Anderson, E.W., Fornell, C., Lehmann, D.R.: Customer satisfaction, market share, and probability: Findings from Sweden. Journal of Marketing 58(4), 53–66 (1994)CrossRefGoogle Scholar
  11. 11.
    Waligora, J., Walligora, R.: Measuring customer satisfaction and loyalty in the automotive industry, aarhus school of business, Denmark, Faculty of business performance management. Journal of Marketing 43(6), 55–69 (2007)Google Scholar
  12. 12.
    Sonne, A.M.: Determinants of customer satisfaction with professional service-a study of consultant services. Journal of Marketing 41(2), 159–171 (1999)Google Scholar
  13. 13.
    Atanassov, K., Szmidt, E., Kacprzyk, J.: On intuitionistic fuzzy pairs. Notes on Intuitionistic Fuzzy Sets 19(3), 1–13 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • S. Rajaprakash
    • 1
  • R. Ponnusamy
    • 2
  • J. Pandurangan
    • 3
  1. 1.Department of computer Science and EngineeringVinayaka Mission UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringRajiv Ganthi College of EngineeringChennaiIndia
  3. 3.Department of Mathematics Aarupadai Veedu Institute of TechnologyVinayaka Mission UniversityChennaiIndia

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