Modelling customer satisfaction for product development using genetic programming

Kit Yan Chan, Chun Kit Kwong, T. C. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)55-68
Number of pages14
JournalJournal of Engineering Design
Issue number1
Publication statusPublished - 1 Jan 2011


  • Customer satisfaction
  • Design attributes
  • Genetic programming
  • Higher-order terms
  • Interaction terms

ASJC Scopus subject areas

  • Engineering(all)

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