Forecasting agricultural product logistics demand by nonlinear principal component analysis and a support vector machine optimised by the grey wolf optimiser
by Xiaoye Zhou; Meilin Zhu; Xiaoyun Ma; Yu Zhong
International Journal of Internet Manufacturing and Services (IJIMS), Vol. 8, No. 2, 2021

Abstract: Artificial intelligence systems can use machine learning algorithms to remarkably improve logistics demand forecasting. This study proposes a novel agri-product logistics demand forecasting model based on a hybrid approach of nonlinear principal component analysis and a grey wolf optimiser-based support vector regression machine. Its performance is investigated experimentally using a case study of agri-product logistics demand in Liaoning Province, China. Comparison with similar models demonstrates that: 1) the proposed model more accurately forecasts agri-product logistics demand; 2) nonlinear principal component analysis significantly outperforms conventional principal component analysis; 3) the grey wolf optimiser greatly improves the performance of the support vector regression machine.

Online publication date: Fri, 06-May-2022

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