Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55296
Title: Intelligent quality function deployment
Authors: Jiang, H
Kwong, CK 
Luo, XG
Keywords: Intelligent techniques
Quality function deployment
Issue Date: 2016
Publisher: Springer Science and Business Media Deutschland GmbH
Source: In C Kahraman & S Yanlik (Eds.), Intelligent decision making in quality management : theory and applications, p. 327-362. Cham: Springer Science and Business Media Deutschland GmbH, 2016 How to cite?
Series/Report no.: Intelligent systems reference library ; v. 97
Abstract: Quality function deployment (QFD) is commonly used in the product planning stage to define the engineering characteristics and target value settings of new products. However, some QFD processes substantially involve human subjective judgment, thus adversely affecting the usefulness of QFD. In recent years, a few studies have been conducted to introduce various intelligent techniques into QFD to address the problems associated with subjective judgment. These studies contribute to the development of intelligent QFD. This chapter presents our recent research on introducing intelligent techniques into QFD with regard to four aspects, namely, determination of importance weights of customer requirements, modeling of functional relationships in QFD, determination of importance weights of engineering characteristics and target value setting of engineering characteristics. In our research, a fuzzy analytic hierarchy process with an extent analysis approach is proposed to determine the importance weights for customer requirements to capture the vagueness of human judgment and a chaos-based fuzzy regression approach is proposed to model the relationships between customer satisfaction and engineering characteristics by which fuzziness and nonlinearity of the modeling can be addressed. To determine importance weights of engineering characteristics, we propose a novel fuzzy group decision-making method to address two types of uncertainties which integrates a fuzzy weighted average method with a consensus ordinal ranking technique. Regarding the target value setting of engineering characteristics, an inexact genetic algorithm is proposed to generate a family of inexact optimal solutions instead of determining one set of exact optimal target values. Possible future research on the development of intelligent QFD is provided in the conclusion section.
URI: http://hdl.handle.net/10397/55296
ISBN: 9783319244990
331924499X
9783319244976
ISSN: 1868-4394
DOI: 10.1007/978-3-319-24499-0_11
Appears in Collections:Book Chapter

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
Citations as of Nov 6, 2017

Page view(s)

41
Last Week
0
Last month
Checked on Nov 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.