IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-0-387-76813-7_12.html
   My bibliography  Save this book chapter

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

In: Fuzzy Multi-Criteria Decision Making

Author

Listed:
  • Ajith Abraham

    (Norwegian University of Science and Technology)

  • Pandian Vasant

    (Universiti Teknologi Petronas)

  • Arijit Bhattacharya

    (Dublin City University, Glasnevin)

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.

Suggested Citation

  • Ajith Abraham & Pandian Vasant & Arijit Bhattacharya, 2008. "Neuro-Fuzzy Approximation of Multi-Criteria Decision-Making QFD Methodology," Springer Optimization and Its Applications, in: Cengiz Kahraman (ed.), Fuzzy Multi-Criteria Decision Making, pages 301-321, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-76813-7_12
    DOI: 10.1007/978-0-387-76813-7_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-0-387-76813-7_12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.