Neuro-Fuzzy Approximation of Multi-Criteria Decision-Making QFD Methodology

  • Ajith Abraham
  • Pandian Vasant
  • Arijit Bhattacharya
Part of the Springer Optimization and Its Applications book series (SOIA, volume 16)


This chapter demonstrates how a neuro-fuzzy approach could produce outputs of a further-modified multi-criteria decision-making (MCDM) quality function deployment (QFD) model within the required error rate. The improved fuzzified MCDM model uses the modified S-curve membership function (MF) as stated in an earlier chapter. The smooth and flexible logistic membership function (MF) finds out fuzziness patterns in disparate level-of-satisfaction for the integrated analytic hierarchy process (AHP-QFD model. The key objective of this chapter is to guide decision makers in finding out the best candidate-alternative robot with a higher degree of satisfaction and with a lesser degree of fuzziness.

Key words

ANFIS AHP QFD fuzziness patterns decision-making level-of-satisfaction 


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Copyright information

© Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Ajith Abraham
    • 1
  • Pandian Vasant
    • 2
  • Arijit Bhattacharya
    • 3
  1. 1.Center of Excellence for Quantifiable Quality of ServiceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Universiti Teknologi PetronasPerak DRMalaysia
  3. 3.School of Mechanical & Manufacturing EngineeringDublin City University, GlasnevinDublin 9Ireland

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