Abstract
Although the product life cycle cost (LCC) is mainly committed by early design stage, designers do not consider the costs caused in subsequent phases of life cycle at this phase. The estimation method for the product life cycle cost in early design processes has been required because of both the lack of detailed information and time for a detailed LCC for a various range of design alternatives. This paper proposes a hybrid genetic algorithm (GA) and artificial neural network (ANN) model to estimate the product LCC that allows the designer to make comparative LCC estimation between the different product concepts. In this study, GAs are employed to select feature subsets eliminated irrelevant factors and determine the number of hidden nodes and processing elements. In addition, GAs are to optimize the connection weights between layers of ANN simultaneously. Experimental results show that a hybrid GA and ANN model outperforms the conventional backpropagation neural network and verify the effectiveness of the proposed method.
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References
Dowlatshahi, S.: Product design in a concurrent engineering environment: an optimisation approach. Journal of Production Research 30(8), 1803–1818 (1992)
Westkämp, E.: Life Cycle Management: Approaches and Visions Towards Sustainable Manufacturing. Annals of the CIRP 49(2), 501–522 (2000)
Westkämp, E., Osten-Sacken, D.v.d.: Product Life Cycle Costing Applied to Manufacturing System. Annals of the CIRP 47(1), 353–356 (1998)
Alting, L.: Life cycle design of products: a new opportunity for manufacturing enterprises. In: Kusiak, A. (ed.) Concurrent Engineering: Automation, Tools, and Techniques, pp. 1–17. Wiley, New York (1993)
Alting, L., Legarth, J.: Life cycle Engineering and Design. Annals of the CIRP 44(2), 569–580 (1995)
Park, J.-H., Seo, K.-K.: Approximate Life Cycle Assessment of Product Concepts using Multiple Regression Analysis and Artificial Neural Networks. KSME International Journal 17(12), 1969–1976 (2003)
Seo, K.-K., Min, S.-H., Yoo, H.-W.: Artificial Neural Network based Life Cycle Assessment Model of Product Concepts using Product Classification Method. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 458–466. Springer, Heidelberg (2005)
Takata, S., Hiraoka, H., Asama, H., Yamoka, N., Saito, D.: Facility model for lifecycle maintenance system. Annals of the CIRP 44(1), 117–121 (1995)
Tarelko, W.: Control model of maintainability level. Reliability Engineering and System Safety 47(2), 85–91 (1995)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)
Adeli, H., Hung, S.: Machine learning: Neural networks, genetic algorithms, and fuzzy systems. Wiley, New York (1995)
Davis, L.: Genetic algorithms and financial applications. In: Deboeck, G.J. (ed.) Trading on the edge, pp. 133–147. Wiley, New York (1994)
Wong, F., Tan, C.: Hybrid neural, genetic, and fuzzy systems. In: Deboeck, G.J. (ed.) Trading on the edge, pp. 243–261. Wiley, New York (1994)
Cooper, D.R., Emory, C.W.: Business research methods. Irwin, Chicago (1995)
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Seo, KK. (2006). A Methodology for Estimating the Product Life Cycle Cost Using a Hybrid GA and ANN Model. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_41
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DOI: https://doi.org/10.1007/11840817_41
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