MFTIWPSO based support vector regression method and its application on supply chain management

Lu Zhang; Wenan Tan; Pan Liu; Dongfang Zhang; Qian Su

doi:10.1117/12.2627308

22 April 2022 MFTIWPSO based support vector regression method and its application on supply chain management

Lu Zhang, Wenan Tan, Pan Liu, Dongfang Zhang, Qian Su

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121630J (2022) https://doi.org/10.1117/12.2627308
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

In supply chain management, it is very important for supply, production and commercial distribution to adopt the optimal method to predict the sales of downstream distributors. MFTIWPSO-SVR model is proposed to predict the sales situation of distributors by using Pearson correlation coefficient at all levels in the supply chain. In this study, the support vector regression (SVR) algorithm is introduced to build the model, and a multi information fusion "triple variables with iteration" inertia weight PSO algorithm (MFTIWPSO) is used to optimize the parameters of SVR model, as well as Pearson correlation coefficient method is used to remove the strong correlation features of supply chain data set in order to determine the appropriate number of features. Experiment results show that the proposed model has higher fitting degree and prediction accuracy compared with the traditional PSO algorithm.

Citation Download Citation

Lu Zhang, Wenan Tan, Pan Liu, Dongfang Zhang, and Qian Su "MFTIWPSO based support vector regression method and its application on supply chain management", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121630J (22 April 2022); https://doi.org/10.1117/12.2627308

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