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- W4312626346 abstract "Smart manufacturing has become mainstream in the development of manufacturing industry, where Industrial Internet of Things plays a critical role. In this article, a systematic intelligent technique for procurement supply chain (PSC) optimization is proposed. In this technique, an integrated approach based on variational mode decomposition and long short-term memory network is used to predict the market price. Considering the factors, such as production plan and market fluctuation, a multiperiod dynamic purchasing model is built. A stacked autoencoder under bootstrap aggregation is then trained to evaluate suppliers automatically end-to-end based on various data. Finally, a multiobjective order allocation model is established considering the procurement costs and supplier scores, and solved by particle swarm optimization. The extensive experiments are performed using a realistic industrial application in a zinc smelter company. The experimental results demonstrate that the proposed technique greatly reduces labor costs, improves the efficiency of PSC, and reduces the procurement costs of the company." @default.
- W4312626346 created "2023-01-05" @default.
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- W4312626346 date "2023-04-15" @default.
- W4312626346 modified "2023-10-16" @default.
- W4312626346 title "A Systematic Procurement Supply Chain Optimization Technique Based on Industrial Internet of Things and Application" @default.
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- W4312626346 doi "https://doi.org/10.1109/jiot.2022.3228736" @default.
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