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Forecasting agricultural product logistics demand by nonlinear principal component analysis and a support vector machine optimised by the grey wolf optimiser

Forecasting agricultural product logistics demand by nonlinear principal component analysis and a... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Internet Manufacturing and Services Inderscience Publishers

Forecasting agricultural product logistics demand by nonlinear principal component analysis and a support vector machine optimised by the grey wolf optimiser

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1751-6048
eISSN
1751-6056
DOI
10.1504/ijims.2021.122717
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Internet Manufacturing and ServicesInderscience Publishers

Published: Jan 1, 2021

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