Development of Product Design Models Using Classical Evolutionary Programming

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)


Chapters 3 and 4 discuss two AHP methods for determining the importance weights for customer requirements of products. When the functional relationship between each individual customer requirement and design attributes of a product is available, the product development teams can maximize the overall customer satisfaction with the new products by optimizing their design attribute settings.


Genetic Programming Customer Satisfaction Functional Model Customer Requirement Quality Function Deployment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Chen, C.H., Khoo, L.P., Yan, W.: An investigation into affective design using sorting technique and Kohonen self-organizing map. Advances in Engineering Software 37, 334–349 (2006)CrossRefGoogle Scholar
  2. Park, J., Han, S.H.: A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics 34, 31–47 (2004)CrossRefGoogle Scholar
  3. Hsiao, S.W., Tsai, H.C.: Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics 35, 411–428 (2005)CrossRefGoogle Scholar
  4. Fung, R.Y.K., Popplewell, K., Xie, J.: An intelligent hybrid system for customer requirements analysis and product attribute targets determination. International Journal of Production Research 36, 13–34 (1998)zbMATHCrossRefGoogle Scholar
  5. Liu, X., Zeng, X., Xu, Y., Koehl, L.: A fuzzy model for customer satisfaction index in e-commerce. Mathematics and Computers in Simulation 77, 512–521 (2007)MathSciNetCrossRefGoogle Scholar
  6. Lin, Y.C., Lai, H.H., Yeh, C.H.: Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones. International Journal of Industrial Ergonomics 37, 531–543 (2007)CrossRefGoogle Scholar
  7. Grigoroudis, E., Siskos, Y.: Preference disaggregation for measuring and analyzing customer satisfaction: The MUSA method. European Journal of Operational Research 143, 148–170 (2002)zbMATHCrossRefGoogle Scholar
  8. Grigoroudis, E., Litos, C., Moustakis, V.A., Politis, Y., Tsironis, L.: The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach. European Journal of Operational Research 187, 1346–1357 (2008)zbMATHCrossRefGoogle Scholar
  9. You, H., Ryu, T., Oh, K., Yun, M.H., Kim, K.J.: Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics 36, 323–330 (2006)CrossRefGoogle Scholar
  10. Han, S.H., Yun, M.H., Kim, K.J., Kwahk, J.: Evaluation of product usability: development and validation of usability dimensions and design elements based on empirical models. International Journal of Industrial Ergonomics 26, 477–488 (2000)CrossRefGoogle Scholar
  11. Kim, K., Park, T.: Determination of an optimal set of design requirements using house of quality. Journal of Operations Management 16, 569–581 (1998)CrossRefGoogle Scholar
  12. Chen, Y., Tang, J., Fung, R.Y.K., Ren, Z.: Fuzzy regression-based mathematical programming model for quality function deployment. International Journal of Production Research 42(5), 1009–1027 (2004)zbMATHCrossRefGoogle Scholar
  13. Chen, Y., Chen, L.: A non-linear possibilistic regression approach to model functional relationships in product planning. International Journal of Advanced Manufacturing Technology 28, 1175–1181 (2006)CrossRefGoogle Scholar
  14. Dawson, D., Askin, R.G.: Optimal new product design using quality function deployment with empirical value functions. Quality and Reliability Engineering International 15, 17–32 (1999)CrossRefGoogle Scholar
  15. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Evolution. MIT Press, Cambridge (1992)Google Scholar
  16. Koza, J.: Genetic Programming II: automatic discovery of reusable programs. MIT Press (1994)Google Scholar
  17. Madar, J., Abonyi, J., Szeifert, F.: Genetic programming for the identification of nonlinear input – output models. Industrial and Engineering Chemistry Research 44, 3178–3186 (2005)CrossRefGoogle Scholar
  18. Rodriguez-Vazquez, K., Fonseca, C.M., Fleming, P.J.: Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans 34(4), 531–545 (2004)CrossRefGoogle Scholar
  19. Lakshminarayanan, S., Fujii, H., Grosman, B., Dassau, E., Lewin, D.R.: New product design via analysis of historical databases. Computers and Chemical Engineering 24, 671–676 (2000)CrossRefGoogle Scholar
  20. Fung, R.Y.K., Chen, Y.Z., Tang, J.F.: Estimating the functional relationships for quality function deployment under uncertainties. Fuzzy Sets and Systems 157, 98–120 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  21. Kwong, C.K., Chen, Y., Bai, H., Chan, D.S.K.: A methodology of determining aggregated importance of engineering characteristics in QFD. Computers and Industrial Engineering 53(4), 667–679 (2007)CrossRefGoogle Scholar
  22. Xu, D., Yan, H.S.: An intelligent estimation method for product design time. International Journal of Advanced Manufacturing Technology 30, 601–613 (2006)CrossRefGoogle Scholar
  23. Billings, S., Korenberg, M., Chen, S.: Identification of nonlinear outputaffine systems using an orthogonal least-squares algorithm. International Journal of Systems Science 19, 1559–1568 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  24. Chen, S., Billings, S., Luo, W.: Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control 50, 1873–1896 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  25. McKay, B., Willis, M.J., Barton, G.W.: Steady-state modeling of chemical processes using genetic programming. Computers and Chemical Engineering 21(9), 981–996 (1997)CrossRefGoogle Scholar
  26. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, Inc., United States of America (1989)zbMATHGoogle Scholar
  27. Seber, G.A.F.: Linear regression analysis. Wiley (2003)Google Scholar
  28. Chen, Y.Z., Fung, R.Y.K., Tang, J.F.: Fuzzy expected value modeling approach for determining target values of engineering characteristics in QFD. International Journal of Production Research 43(17), 3583–3604 (2005)zbMATHCrossRefGoogle Scholar
  29. Chan, K.Y., Kwong, C.K., Wong, T.C.: Modelling customer satisfaction for product development using genetic programming 22(1), 55–68 (2011)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

Personalised recommendations