A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control. (August 2023) - Nie, Qingwei
Highlights: The cloud-edge orchestration architecture for distributed production control provides a reference for future designs. A global optimized model for assigning manufacturing tasks to the distributed factories and a CNN-BiLSTM-Attention-based rescheduling decision prediction for overall control adjustment at the cloud is developed. The self-adaptive strategy at the edge is introduced, which shows how to construct a distributed manufacturing system with high autonomy and adaptability. A novel way of orchestrating edge computing and cloud computing is proposed to tighten the top-level cloud production line and the bottom-level distributed manufacturing resources. Abstract: 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 resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding 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 holistic 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.