Reinforcement learning-based dynamic production-logistics-integrated tasks allocation in smart factories

Lei, Jingyuan; Jing, Hui; Chang, Fengtian; Dassari, Salim; Ding, Kai

doi:10.1080/00207543.2022.2142314

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…In smart factories, RL can be applied to optimize production scheduling, formulate equipment control strategies, and optimize resource allocation. Its advantage lies in its ability to autonomously learn and adapt to complex production environments (Lei et al, 2023).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

Wang,

Hu,

Wang

et al. 2024

Journal of Organizational and End User Computing

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Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%