Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 765956, 8 pages
http://dx.doi.org/10.1155/2013/765956
Research Article

Reduced Multivariate Polynomial Model for Manufacturing Costs Estimation of Piping Elements

1School of Computer Science Engineering, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
2School of Mechanical Engineering, Pontificia Universidad Católica de Valparaíso, 2430120 Valparaíso, Chile

Received 10 January 2013; Revised 18 March 2013; Accepted 18 March 2013

Academic Editor: M. Onder Efe

Copyright © 2013 Nibaldo Rodriguez and Orlando Duran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Curran, S. Raghunathan, and M. Price, “Review of aerospace engineering cost modelling: the genetic causal approach,” Progress in Aerospace Sciences, vol. 40, no. 8, pp. 487–534, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Graham and S. D. Smith, “Estimating the productivity of cyclic construction operations using case-based reasoning,” Advanced Engineering Informatics, vol. 18, no. 1, pp. 17–28, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Bode, “Neural networks for cost estimation: simulations and pilot application,” International Journal of Production Research, vol. 38, no. 6, pp. 1231–1254, 2000. View at Google Scholar · View at Scopus
  4. E. Shehab and H. Abdalla, “An intelligent knowledge-based system for product cost modelling,” International Journal of Advanced Manufacturing Technology, vol. 19, no. 1, pp. 49–65, 2002. View at Google Scholar · View at Scopus
  5. M. Ficko, I. Drstvenšek, M. Brezočnik, J. Balič, and B. Vaupotic, “Prediction of total manufacturing costs for stamping tool on the basis of CAD-model of finished product,” Journal of Materials Processing Technology, vol. 164-165, pp. 1327–1335, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Cavalieri, P. Maccarrone, and R. Pinto, “Parametric vs. neural network models for the estimation of production costs: a case study in the automotive industry,” International Journal of Production Economics, vol. 91, no. 2, pp. 165–177, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Verlinden, J. R. Duflou, P. Collin, and D. Cattrysse, “Cost estimation for sheet metal parts using multiple regression and artificial neural networks: a case study,” International Journal of Production Economics, vol. 111, no. 2, pp. 484–492, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. J. de Cos, F. Sanchez, F. Ortega, and V. Montequin, “Rapid cost estimation of metallic components for the aerospace industry,” International Journal of Production Economics, vol. 112, no. 1, pp. 470–482, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. H. Che, “PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding,” Computers and Industrial Engineering, vol. 58, no. 4, pp. 625–637, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Deng and T. H. Yeh, “Applying least squares support vector machines to the airframe wing-box structural design cost estimation,” Expert Systems with Applications, vol. 37, no. 12, pp. 8417–8423, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. K. J. Kim and I. Han, “Application of a hybrid genetic algorithm and neural network approach in activity-based costing,” Expert Systems with Applications, vol. 24, no. 1, pp. 73–77, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Deng and T. H. Yeh, “Using least squares support vector machines for the airframe structures manufacturing cost estimation,” International Journal of Production Economics, vol. 131, no. 2, pp. 701–708, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Rui, P. A. Metz, and G. Chen, “Regressions allow development of compressor cost estimation models,” Oil & Gas Journal, vol. 110, no. 1, pp. 110–115, 2012. View at Google Scholar
  14. C. G. Hart, Z. He, R. Sbragio et al., “An advanced cost estimation methodology for engineering systems,” Systems Engineering, vol. 15, no. 1, pp. 28–40, 2012. View at Google Scholar
  15. Z. Rui, G. Metz, and P. A. Chen, “An analysis of inaccuracy in pipeline construction cost estimation,” International Journal of Oil Gas and Coal Technology, vol. 5, no. 1, pp. 29–46, 2012. View at Google Scholar
  16. D. Coleman, P. Holland, N. Kaden, V. Klema, and S. C. Peters, “System of subroutines for iteratively reweighted least squares computations,” ACM Transactions on Mathematical Software, vol. 6, no. 3, pp. 327–336, 1980. View at Publisher · View at Google Scholar · View at Scopus
  17. K. A. Toh, W. Y. Yau, and X. Jiang, “A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 2, pp. 224–233, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994. View at Publisher · View at Google Scholar · View at Scopus
  19. J. O. Street, R. J. Carroll, and D. Ruppert, “A note on computing robust regression estimates via iteratively reweighted least squares,” The American Statistician, vol. 42, pp. 152–154, 1998. View at Google Scholar
  20. D. Serre, Matrices: Theory and Applications, Springer, New York, NY, USA, 2002.