Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

被引:24
|
作者
Ma, Jing [1 ]
Zhou, Hua [1 ]
Liu, Changchun [2 ]
E, Mingcheng [1 ]
Jiang, Zengqiang [1 ]
Wang, Qiang [3 ]
机构
[1] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China
[2] Natl Univ Singapore, Inst Operat Res & Analyt, Singapore 117602, Singapore
[3] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
基金
美国国家科学基金会;
关键词
Production scheduling; edge-cloud collaboration; edge computing; Internet of Things; GENETIC ALGORITHM; STRATEGY;
D O I
10.1109/ACCESS.2020.2972914
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:30069 / 30080
页数:12
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