Linear programming embedded genetic algorithm for product family design optimization with maximizing imprecise part-worth utility function

Xinggang Luo, Wei Yang, Chun Kit Kwong, Jianguo Tang, Jiafu Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Product family design optimization is an important decision task in the early stages of product development. The extant optimization models for product family design assume that the known information for modeling can be determined precisely. However, many collected information for product design are prone to be imprecise due to the inherent uncertainty of human knowledge and expression. For example, when human experts estimate market demand of a product, imprecise information may be involved and influence the results of optimization. In this research, an optimization model with maximizing imprecise part-worth utility function is established for product family design problem. A linear programming embedded genetic algorithm is proposed to solve the proposed fuzzy optimization model. An industrial case of printing calculator product is used to illustrate the proposed approach. Experiments and sensitivity analysis based on the case study are also performed to analyze the relationship among the parameters and to explore the characteristics of the optimization model.
Original languageEnglish
Pages (from-to)309-319
Number of pages11
JournalConcurrent Engineering Research and Applications
Volume22
Issue number4
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • customer preference
  • genetic algorithm
  • imprecision
  • optimization
  • Product family

ASJC Scopus subject areas

  • Modelling and Simulation
  • Engineering(all)
  • Computer Science Applications

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