A methodology for estimating the product life cycle cost using a hybrid GA and ANN model

Authors:
Kwang-Kyu Seo
Affiliations:
Department of Industrial Information and Systems Engineering, Sangmyung University, Chungnam, Korea
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 3
Cited 0

Machine learning: neural networks, genetic algorithms, and fuzzy systems

Machine learning: neural networks, genetic algorithms, and fuzzy systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Artificial neural network based life cycle assessment model for product concepts using product classification method

ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV

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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.