Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30735
Title: A multiobjective optimization-based neural network model for short-term replenishment forecasting in fashion industry
Authors: Du, W
Leung, SYS 
Kwong, CK 
Keywords: Fashion sales forecasting
Multiobjective optimization
Neural network
Short-term replenishment forecasting
Issue Date: 2015
Publisher: Elsevier
Source: Neurocomputing, 2015, v. 151, no. P1, p. 342-353 How to cite?
Journal: Neurocomputing 
Abstract: A multiobjective optimization-based neural network (MOONN) model is proposed to tackle the short-term replenishment forecasting problem in fashion industry. Our approach utilizes a new multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution algorithm (NSJADE) to optimize the weights of neural networks (NNs) for the short-term replenishment forecasting problem, acquiring the forecasting accuracy while alleviating the overfitting effect at the same time. The presented NSJADE also selects the appropriate number of hidden nodes for the NN according to different short-term replenishment forecasting problems. Extensive experiments based on real fashion industry data are performed to validate the effectiveness of the developed model. Experimental results reveal that the performance of the proposed model is superior than several popular models for the short-term replenishment forecasting problem.
URI: http://hdl.handle.net/10397/30735
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2014.09.030
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