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https://doi.org/10.1109/access.2020.2972914
Title: | Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises with Multi-Factory | Authors: | Ma, Jing Zhou, Hua Liu, Changchun E. Mingcheng Jiang, Zengqiang Wang, Qiang |
Issue Date: | 2020 | Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | Citation: | Ma, Jing, Zhou, Hua, Liu, Changchun, E. Mingcheng, Jiang, Zengqiang, Wang, Qiang (2020). Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises with Multi-Factory. IEEE Access : 1-1. ScholarBank@NUS Repository. https://doi.org/10.1109/access.2020.2972914 | Abstract: | The depth development and widespread application of edge intelligence technology based on the Internet of Things has led to edge-cloud collaboration and related research. In recent years, with the rapid development of the Internet of Things and the formation of super-city groups, the management characteristics of enterprises with multiple manufacturing plants served for headquarters have become increasingly obvious. The problem of order dynamic fluctuations caused by personalized customization requirements has become more prominent, which makes it impossible to do global long-period prediction or real-time short-period response relied solely on the cloud or edge. Therefore, this paper proposes a production system scheduling framework under the edge-cloud collaborative paradigm based on the dynamic fluctuation of orders under these background, and builds an edge-cloud collaborative scheduling model, which guarantees real-time distributed scheduling at the edge. It enabled the cloud to periodically predict the total completion time of production tasks at the headquarters based on the value-added data uploaded by the edge, and to support more accurate and efficient scheduling at the edge based on the prediction results. Finally, an example analysis proved the rationality of the scheduling mechanism and the effectiveness of the scheduling model. The proposed method can provide a certain reference for task scheduling in the edge-cloud collaborative production paradigm. | Source Title: | IEEE Access | URI: | https://scholarbank.nus.edu.sg/handle/10635/164468 | ISSN: | 21693536 | DOI: | 10.1109/access.2020.2972914 |
Appears in Collections: | Staff Publications Elements |
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