Affiliations: [a] Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran | [b] Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | [c] Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis new city, Tehran, Iran
Correspondence:
[*]
Corresponding author. S. Meysam Mousavi, Department ofIndustrial Engineering, Faculty of Engineering, Shahed University,Tehran, Iran. Tel.: +98 21 51212091; [email protected]
Abstract: Precise cost prediction of new product development (NPD) projects has been a challenge for both academia and practitioners that often requires much effort and experience. In this paper, a combination of particle swarm optimization (PSO), cross validation (CV) and support vector regression (SVR) is proposed to predict the cost of NPD projects. SVR, a novel intelligent technique for time series analysis, can overcome some shortcomings in the conventional approaches; and PSO, a new evolutionary computation technique, is utilized to set the optimal parameters of the SVR. The proposed intelligent model avoids manual selection of these parameters. The PSO solves the difficulty of setting these parameters appropriately and enhances the efficiency and capability of cost prediction. In addition, the CV is employed to train the SVR and improve the reliability of model performance. Then a real dataset of a home appliances manufacturer is provided to illustrate the proposed model and demonstrate the high performance and applicability to cost prediction of the NPD project. Finally, the effectiveness of the support vector model is compared with well-known techniques including multilayer perceptron networks (MLP), normalized radial basis function (NRBF) neural network, and pure SVR in terms of the accuracy measures. Based on the real world dataset, it is observed that the proposed model outperforms other well-known techniques.
Keywords: New product development, cost prediction, neural networks, support vector regression, cross validation, particle swarm optimization
