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A Genetic Algorithm for Scale-Based Product Platform Planning

  • Zhen Lu
  • Jiang Zuhua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Product platform planning paves the way for DFMC (Design for Mass Customization). In this paper, we mainly focus on the development of a method for scale-based product platform planning using genetic algorithm (GA) to satisfy a set of customer requirements. Different from many usual methods for platform planning that need users to specify product platform prior to optimize it, the new GA-based method proposed by this paper focuses on searching a proper balance between the commonality of product platform and the performance of product family derived from the platform. The method improves as more commonality of the product platform as possible, within the satisfactory of the diverse customer needs, and then determines the variable product attributes and their variation ranges, and the common parameters of product platform and their optimal values. This method of product platform planning is validated by a case study of the small-size induction motor design. At last we compare the results from our GA-based method with the benchmark products that are individually designed to the optimal performance.

Keywords

Design Variable Product Family Mass Customization Product Platform Customer Group 
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.

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References

  1. 1.
    Jianxin, J.: Design for Mass Customization by Developing Product Family Architecture. A dissertation of PhD of the Hong Kong university of science and technology (1998) Google Scholar
  2. 2.
    Meyer, M., Lehnerd, A.: The Power of Product Platforms. The Free Press, New York (1997)Google Scholar
  3. 3.
    Simpson, T.W., Maier, J.R.A., Mistree, F.A.: Product platform design: method and application. Research in Engineering Design 13(1), 2–22 (2001)CrossRefGoogle Scholar
  4. 4.
    Sabbagh, K.: Twenty-first century jet: the market and marketing of Boeing, vol. 777. Scribner, New York (1996)Google Scholar
  5. 5.
    Hernandez, G., Allen, J.K., Simpson, T.W., et al.: Robust design of family for products with production modeling and revaluation [J]. ASME Journal of Mechanical Design 6, 183–190 (2001)CrossRefGoogle Scholar
  6. 6.
    Nayak, R.U., Chen, W., Simpson, T.W.: A variation-based method for product family design. Engineering Optimization 34(1), 65–81 (2002)CrossRefGoogle Scholar
  7. 7.
    Simpson, T.W., Maier, J.R.A., Mistree, F.A.: Product platform concept exploration method for product family design. ASME Design Theory and Methodology (9), 1–219 (1999)Google Scholar
  8. 8.
    Tseng, M.M., Jianxin, J.: Design for mass customization. Annuals of CIRP 45(1), 153–156 (1996)CrossRefGoogle Scholar
  9. 9.
    Han, J., Kamber, M.: Data mining. Concepts and techniques. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  10. 10.
    Gonzalez-Zugasti, J.P., Otto, K.N., et al.: A method for architecting product platforms. Research in Engineering Design 12(1), 61–72 (2000)CrossRefGoogle Scholar
  11. 11.
    Messac, A., Martinez, M.P., Simpson, T.W.: Effective product family design using physical programming. Engineering Optimization 34(3), 245–261 (2002)CrossRefGoogle Scholar
  12. 12.
    Muhlenbein, H.: Evolutionary algorithm: theory and applications. GMD Birlinhoven (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhen Lu
    • 1
  • Jiang Zuhua
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiP.R.C.

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