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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2021

19.06.2020 | S.I. : DPTA Conference 2019

Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set

verfasst von: Jianli Gao

Erschienen in: Neural Computing and Applications | Ausgabe 2/2021

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Abstract

The collaborative logistics in manufacturing industry has a greater impact on its operation effect, and there are many hidden factors. In order to improve the performance evaluation of manufacturing collaborative logistics, this study builds a combined performance evaluation model based on BP neural network and rough set. Moreover, this study uses the rough set attribute reduction theory to screen and optimize the evaluation indicators to obtain the key performance indicator set, and then uses BP neural network to predict and evaluate the key performance indicator data, which greatly reduces the number of training times and shortens the learning time. In addition, in this study, a case analysis was used to solve the performance evaluation model of manufacturing collaborative logistics based on rough set and BP neural network, and corresponding strategies were given. The research results show that the method proposed in this paper has certain effects.
Vorheriger Artikel Exploring the financial indicators to improve the pattern recognition of economic data based on machine learning
Nächster Artikel Optimizing deep learning and neural network to explore enterprise technology innovation model

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Metadaten
Titel
Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set
verfasst von
Jianli Gao
Publikationsdatum
19.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05099-9

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