Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/32387
Title: | Modelling customer satisfaction for product development using genetic programming | Authors: | Chan, KY Kwong, CK Wong, TC |
Keywords: | Genetic programming Interaction terms Higher-order terms Customer satisfaction Design attributes |
Issue Date: | 2011 | Publisher: | Taylor & Francis | Source: | Journal of engineering design, 2011, v. 22, no. 1, p. 55-68 How to cite? | Journal: | Journal of engineering design | Abstract: | Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained. To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method. | URI: | http://hdl.handle.net/10397/32387 | ISSN: | 0954-4828 | DOI: | 10.1080/09544820902911374 |
Appears in Collections: | Journal/Magazine Article |
Show full item record
SCOPUSTM
Citations
27
Last Week
0
0
Last month
0
0
Citations as of Apr 16, 2018
WEB OF SCIENCETM
Citations
22
Last Week
0
0
Last month
1
1
Citations as of Apr 15, 2018
Page view(s)
52
Last Week
0
0
Last month
Citations as of Apr 16, 2018

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