Regression models for estimating product life cycle cost

• Aha D., Kibler D., Albert M. (1991) Instance-based learning algorithms. Machine Learning 6: 37–66

  Google Scholar 

• Artto K.A. (1994) Life cycle cost concepts and methodologies. Journal of Cost Management 8(4): 28–32

  Google Scholar 

• Asiedu Y., Gu P. (1998) Product life cycle cost analysis: state of the art review. International Journal of Production Research 36(4): 883–908

  Article  Google Scholar 

• Brass, J., Gerrard, A. M., & Peel, D. (1994). Estimating vessel costs via neural networks. In Proceedings of the 13th International Cost Engineering Congress, London.

• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees, Wadsworth.

• Cavalieri S., Maccarrone P., Rinto R. (2004) Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics 91: 165–177

  Article  Google Scholar 

• Dowlatshahi S. (1992) Product design in a concurrent engineering environment: an optimization approach. International Journal of Production Research 30(8): 1803–1818

  Article  Google Scholar 

• Hornic K., Stinchcombe M., White H. (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2: 359–366

  Article  Google Scholar 

• Lawrence, J. (1994). Introduction to neural networks: design, theory, and applications, California Scientific Software, Nevada City, CA.

• Lawrence, J., & Fredrickson, J. (1998). BrainMaker user’s guide and reference manual, California Scientific Software, Nevada City, CA.

• Layer A., Brinke E.T., Houten F.V., Kals H., Haasis S. (2002) Recent and future trends in cost estimation. Journal of Computer Integrated Manufacturing 15(6): 499–510

  Article  Google Scholar 

• Lin Z.-C., Change D.-Y. (2002) Cost-tolerance analysis model based on a neural networks method. International Journal of Production Research 40(6): 1429–1452

  Article  Google Scholar 

• Lotfy E.A., Mohamed A.S. (2002) Applying neural networks in case-based reasoning adaptation for cost assessment of steel buildings. International Journal of Computational and Numerical Analysis and Applications 24(1): 28–38

  Google Scholar 

• Mitchell, T. (1997). Machine learning, McGraw Hill.

• Niazi A., Dai J.S., Balabani S., Seneviratne L. (2006) Product cost estimation: technique classification and methodology review. Journal of Manufacturing Science and Engineering 128: 563–575

  Article  Google Scholar 

• Perera H.S.C., Nagarur N., Tabucanon M.T. (1999) Component part standardization: a way to reduce the life-cycle costs of products. International Journal of Production Economics 60(4): 109–116

  Article  Google Scholar 

• Smith A., Mason A. (1997) Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist 42: 137–161

  Article  Google Scholar 

• Smola, A. J., & Scholkop, B. (1998). A tutorial on support vector regression. NeuroCOLT2 Technical Report Series–NC2-TR-1998-030.

• Twomey J.M., Smith A.E. (1998) Bias and variance of validation methods for function approximation neural networks under conditions of sparse data. IEEE Transactions on Systems, Man and Cybernetics 28(3): 417–430

  Google Scholar 

• Uysal I., Guvenir H.A. (1999) An overview of regression techn- iques for knowledge discovery. The Knowledge Engineering Review 14(4): 319–340

  Article  Google Scholar 

• Witten I.H., Frank E. (2005) Data mining: Practical machine lear- ning tools and techniques. Morgan Kaufmann, San Francisco

  Google Scholar 

• Xu X., Chen J.-Q., Xie S. (2006) Framework of a product lifecycle costing system. Journal of Computing and Information Science in Engineering 6: 69–77

  Article  Google Scholar 

Download references