Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach

Shuya Zhong, Jian Zhou, Yizeng Chen

Research output: Contribution to journalArticlepeer-review

28 Citations (SciVal)


Quality function deployment (QFD) is a method used for the manufacturing process of a product or service that is devoted to transforming customer requirements (CRs) into appropriate engineering characteristics (ECs) by specifying the importance of the ECs and then setting their target values. Confronting the inherent vagueness or impreciseness in the QFD process, we embed the fuzzy set theory into QFD. A fuzzy chance-constrained modelling approach with core philosophies of fuzzy expected value model and fuzzy chance-constrained programming is used in this paper. Thus, a novel fuzzy chance-constrained programming model whose objective is to minimize the fuzzy expected cost is proposed to determine the target values of the ECs with risk control to ensure satisfying CRs. Meanwhile, when considering the importance of the ECs, we adopt a more reasonable dispose which is to aggregate the relationships between the CRs and the ECs, and the correlations among the ECs. In order to solve the presented model, a hybrid intelligent algorithm is designed by integrating fuzzy simulation and genetic algorithm. Finally, an example of a motor car design is given to demonstrate the feasibility and effectiveness of the devised modelling approach and algorithm.

Original languageEnglish
Pages (from-to)125-135
Number of pages11
Early online date29 May 2014
Publication statusPublished - 22 Oct 2014

Bibliographical note

Funding Information:
This work was supported by grants from the National Natural Science Foundation of China (No. 71272177 ), the National Social Science Foundation of China (No. 13CGL057 ), the Ministry of Education Funded Project for Humanities and Social Sciences Research (No. 12JDXF005).


  • Fuzzy chance-constrained programming
  • Genetic algorithm
  • Product design
  • Quality function deployment
  • Simulation
  • Targets setting

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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