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Academic Journal of Computing & Information Science, 2021, 4(8); doi: 10.25236/AJCIS.2021.040801.

Research on Material Ordering and Transportation Issues Based on RBF Neural Network and 0-1 Planning Model

Author(s)

Shengjie Chen, Li Ding, Tianbo Zhang, Kechi Jiang

Corresponding Author:
Shengjie Chen
Affiliation(s)

College of Civil Engineering and Architecture, Hebei University, Baoding, Hebei, 071002, China

Abstract

To select the best 50 from 402 suppliers, this article mainly establishes a principal component comprehensive evaluation model to formulate the five most important supplier service indicators. To reflect the impact of timeliness on the modeling results, time weights are introduced for dynamic weighing. The RBF neural network model is used to predict the supply and order situation in the next 24 weeks. According to its time-weighted average supply capacity, two forecast corrections are made to make the forecast results more reliable. Establish a 0-1 planning model to decide on the most economical ordering plan and the transfer plan with the least loss.

Keywords

PCA, RBF neural network, 0-1 planning

Cite This Paper

Shengjie Chen, Li Ding, Tianbo Zhang, Kechi Jiang. Research on Material Ordering and Transportation Issues Based on RBF Neural Network and 0-1 Planning Model. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 8: 1-4. https://doi.org/10.25236/AJCIS.2021.040801.

References

[1] Li Jing. TOPSIS Evaluation Method of Financial Management System Based on Dynamic Weighted Sequence [J]. Business management, 2021(2016-21): 171-173.

[2] SONG Yi-bin, WANG Pei-jin. An improved RBF neural network for predictive control model [J]. J. CENT. SOUTH UNIV. (SCIENCE AND TECHNOLOGY), 2005(36): 76-80.