Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology

Ajith Abraham, Pandian Vasant, Arijit Bhattacharya

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing AG
Pages301-321
Number of pages21
ISBN (Print)978-0-387-76812-0
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
Volume16
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Keywords

  • AHP
  • ANFIS
  • Decision-making
  • Fuzziness patterns
  • Level-of-satisfaction
  • QFD

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