A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing re-sources are collected and processed to perform the decision-making and manufacturing execution by the cor-responding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self -adaptive strategy that makes the real-time exceptions results available on the cloud production line for holis-tic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.

Automated summary

This paper proposes a multi-agent and cloud-edge orchestration framework for production control in the distributed manufacturing environment. Real-time status data of the distributed manufacturing resources are collected and processed to perform decision-making and manufacturing execution. The cloud production line model is established to support the optimal configuration of distributed idle manufacturing resources based on the generated data.