Implementing a diffusion model optimized by a hybrid evolutionary algorithm to forecast notebook shipments

Authors:
Fu-Kwun Wang;Ku-Kuang Chang;Yu-Yao Hsiao
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan;Department of Logistic Management, Takming University of Science and Technology, Taiwan;Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan
Venue:
Applied Soft Computing
Year:
2013

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Abstract

Taiwan computer firms need to forecast trends in notebook shipments. The Bass diffusion model has been successfully applied to describe the empirical adoption curve for many new products and technological innovations. In order to improve the parameter estimates, a hybrid evolutionary algorithm, which couples genetic algorithms (GAs) with particle swarm optimization (PSO), is proposed. This hybrid approach can produce more accurate estimates of the parameters for the Bass diffusion model. In addition, the price index plays an important role in the notebook market. Thus, the modified diffusion model is proposed to investigate the forecasting performance for notebook shipments. The results illustrate that a hybrid approach outperforms other methods such as nonlinear algorithm, GA and PSO in terms of mean absolute percentage error.