Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process.

如今，网络物理生产系统的一个重要挑战是通过在生产运行时执行的自动化决策来更新动态生产计划。制造设备的状况实际上可能导致计划不可行或效率低下，因此需要响应以保持生产力并降低运营成本。为了解决当前传统调度方法的局限性，这项工作提出了一个新框架，该框架利用多个数字孪生的聚合，代表不同的物理资产及其自主决策，以及全球数字孪生，以执行生产调度需要时进行优化。决策过程由模糊推理系统支持，该系统使用不同资产的状态或条件以及整个系统的生产率。资产状况由工作站本地数字孪生中的状态监控模块预测，而生产率则由车间的全球数字孪生评估和保证。本文提出了一个用于重新安排网络物理生产系统的分散和集成决策框架，以及在工业 4.0 装配过程试验线中验证和验证所提出方法的概念。实验结果表明，所提出的框架能够检测制造过程中的变化，并为重新安排过程做出适当的决定。