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Using least squares support vector machines for the airframe structures manufacturing cost estimation

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  • Deng, S.
  • Yeh, Tsung-Han

Abstract

Accurate cost estimation plays a significant role in industrial product development and production. This research applied least squares support vector machines (LS-SVM) method solving the problem of estimating the manufacturing cost for airframe structural projects. This research evaluated the estimation performance using back-propagation neural networks and statistical regression analysis. In case studies, this research considered structural weight and manufacturing complexity as the main factors in determining the manufacturing labor hour. The test results verified that the LS-SVM model can provide accurate estimation performance and outperform other methods. This research provides a feasible solution for airframe manufacture industry.

Suggested Citation

  • Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    • Handle: RePEc:eee:proeco:v:131:y:2011:i:2:p:701-708
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    References listed on IDEAS

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    1. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    2. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
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