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

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.