An integrated intelligent manufacturing model based on scheduling and reinforced learning algorithms

Liu, Chang; Luosang, Renqing; Yao, Xing; Su, Letian

doi:10.1016/j.cie.2021.107193

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Processes 2023, 11,1482 This is further studied using the blockchain output for the logs (α, β). In this process, W is the modification, and T for preventing sustainability falls (Table 2).…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0

Sun

Diao

2023

Processes

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Intelligent manufacturing under Industry 4.0 assimilates sophisticated technologies and artificial intelligence for sustainable production and outcomes. Blockchain paradigms are coined with Industry 4.0 for concurrent and well-monitored flawless production. This article introduces Sustainable Production concerned with External Demands (SP-ED). This method is more specific about energy production and the distribution for flawless and outage-less supply. First, the energy demand is identified for internal and external users based on which sustainability is planned. Secondly, Ethereum blockchain monitoring for a similar production and demand satisfaction is coupled with the production system. From two perspectives, the monitoring and condition satisfaction processes are validated using federated learning (FL). The perspectives include demand distribution and production sustainability. In the demand distribution, the condition of meeting the actual requirement is validated. Contrarily, the flaws in internal and external supply due to production are identified in sustainability. The failing conditions in both perspectives are handled using blockchain records. The blockchain records reduce flaws in the new production by modifying the production plan according to the federated learning verifications. Therefore, the sustainability for internal and external demands is met through FL and blockchain integration.

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Section: Dataset Descriptionmentioning

confidence: 99%

“…CNN uses a feature extraction method that extracts the essential features and patterns from the database. CNN reduces the energy consumption range in computation, improving the efficiency ratio in the energy production process [11,12]. The support vector regression (SVR) technique is also used in sustainable production.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0

Sun

Diao

2023

Processes

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Applying the blockchain-based deep reinforcement consensus algorithm to the intelligent manufacturing model under internet of things

Geng

2022

J Supercomput

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Optimizing the production process of modular construction using an assembly line-integrated supermarket

Jin-feng

Zhao²,

Liu³

2022

Automation in Construction

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An integrated intelligent manufacturing model based on scheduling and reinforced learning algorithms

Cited by 7 publications

References 20 publications

Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0

Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0

Applying the blockchain-based deep reinforcement consensus algorithm to the intelligent manufacturing model under internet of things

Optimizing the production process of modular construction using an assembly line-integrated supermarket

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