eprintid: 488
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/04/88
datestamp: 2023-11-09 15:16:07
lastmod: 2023-11-09 15:16:07
status_changed: 2023-11-09 15:14:40
type: article
metadata_visibility: show
creators_name: Abraham, A.
creators_name: Vasant, P.
creators_name: Bhattacharya, A.
title: Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology
ispublished: pub
note: cited By 9
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. © Springer Science + Business Media, LLC 2008.
date: 2008
publisher: Springer International Publishing
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976463038&doi=10.1007%2f978-0-387-76813-7_12&partnerID=40&md5=92fb8a20e07b9b8bab459a966f8a0cba
id_number: 10.1007/978-0-387-76813-7₁₂
full_text_status: none
publication: Springer Optimization and Its Applications
volume: 16
pagerange: 301-321
refereed: TRUE
issn: 19316828
citation:   Abraham, A. and Vasant, P. and Bhattacharya, A.  (2008) Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology.  Springer Optimization and Its Applications, 16.  pp. 301-321.  ISSN 19316828