AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products

Chun Kit Kwong, Huimin Jiang, X. G. Luo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)

Abstract

In the early stage of product design, particularly for consumer products, affective design, engineering, and marketing issues must be taken into considerationand they are commonly performed respectively by product designers, engineers, and marketing personnel. However, they have different concerns and focuses with regard to the new product design. Thus, these three processes are commonly conducted separately, leading to a sub-optimal and even sub-standard design. Such scenario indicates the need to incorporate the concerns of the three processes in the early stage of product design. However, no study has explored the incorporation of the concerns of the three processes into the product design. In this paper, an artificial intelligence (AI)-based methodology for integrating affective design, engineering, and marketing for defining design specifications of new products is proposed by which the concerns of the three processes can be considered simultaneously in the early design stage. The proposed methodology mainly involves development of customer satisfaction and cost models using fuzzy regression, generation of product utility functions using chaos-based fuzzy regression, formulation of a multi-objective optimization model and its solving using a non-dominated sorting genetic algorithm-II (NSGA-II). A case study was conducted for electric iron design to evaluate the effectiveness of the proposed methodology.
Original languageEnglish
Pages (from-to)49-60
Number of pages12
JournalEngineering Applications of Artificial Intelligence
Volume47
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • Affective design
  • Chaos optimization algorithm
  • Fuzzy regression
  • Marketing
  • NSGA-II

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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