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Research on manufacturing service combination optimization based on neural network and multi-attribute decision making

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Abstract

As an important branch of modern decision-making science, the theory and method of multi-attribute decision making have been widely used in many fields, such as society, economy, management and military affairs. Automobile industry as one of the typical manufacturing industries, in this wave, ushered in a transformation and upgrade to get rid of the current weaknesses and difficulties of rare historical development opportunities. Under the environment of Internet and big data, the construction of a new production and operation organization model of the smart car industry will promote its transformation in product planning, design, production, marketing and operation and maintenance. It is propitious to realize the service innovation facing the whole life cycle of the product. In view of this, this paper applies multi-attribute decision making to automobile manufacturing and service industry, taking 14 attributes of manufacturing and six attributes of automobile service as input, and training by BP neural. Finally, an example is given to verify the effectiveness of the method, and the average accuracy is 93.19%.

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Acknowledgements

This work was supported by Soft Scientific Research Projects in Henan Province, China (ID: 172400410013), Special Project on Innovation Method Work of China Ministry of Science and Technology (ID: 2017IM060100) and Key Scientific Research Projects in Henan Province, China (ID: 17A630016).

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Authors and Affiliations

  1. Henan University of Science and Technology, Luoyang, China

    Mei Yang, Hai Zhu & Kai Guo

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  1. Mei Yang
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  2. Hai Zhu
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  3. Kai Guo
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Correspondence to Mei Yang.

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Yang, M., Zhu, H. & Guo, K. Research on manufacturing service combination optimization based on neural network and multi-attribute decision making. Neural Comput & Applic 32, 1691–1700 (2020). https://doi.org/10.1007/s00521-019-04241-6

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  • DOI: https://doi.org/10.1007/s00521-019-04241-6

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