Strategy transformation of big data green supply chain by using improved genetic optimization algorithm

被引：1
|
作者：
Zhang, Peng [1 ]
Dong, Yanli [1 ]
机构：
[1] Weifang Univ Sci & Technol, Sch Econ & Management, Weifang 262700, Shandong, Peoples R China
来源：
SOFT COMPUTING | 2023年
关键词：
Wireless technology; Mobile network; Synchronous detection; Brand management;
D O I：
10.1007/s00500-023-08911-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The change of big data green supply chain strategy based on improved genetic optimization algorithm is one of the hot topics in green supply chain management. In order to realize the sustainable development of green supply chain, this paper constructs a big data green supply chain system based on improved genetic optimization algorithm. First of all, it is necessary to collect relevant data and pre-process the collected data to ensure the quality and consistency of the data. Then a mathematical model is established based on improved genetic optimization algorithm to describe the operation process of green supply chain. The improved genetic optimization algorithm can improve the search efficiency and optimization performance of genetic algorithm by improving genetic operators and adjusting parameters. Thirdly, the optimization model is solved to obtain the optimal supply chain scheme. Finally, the solution results are analyzed and evaluated to evaluate the optimization effect of green supply chain. The results show that the strategy change of big data green supply chain based on improved genetic optimization algorithm is one of the important means to realize the sustainable development of green supply chain. The big data green supply chain system based on improved genetic optimization algorithm constructed in this paper can optimize and improve the green supply chain, improve the operational efficiency and environmental performance of the green supply chain, and provide strong support for the sustainable development of the green supply chain.
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页数：10
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