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A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins

机译：基于数字双胞胎的网络物理生产系统动态调度决策框架

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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 decisionmaking 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 proofof-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.
机译：如今，网络物理生产系统中的一个重要挑战正在通过生产正在运行的自动决策进行动态生产计划。制造设备的状况实际上可能导致不可行或低效率，从而要求响应性以维护生产力并降低运营成本。为了解决传统调度方法的当前限制，这项工作提出了一种新的框架，该框架利用了几个数字双胞胎的聚合，代表了不同的物理资产和自主决策以及全球数字双胞胎，以便进行生产计划需要时优化。使用不同资产的状态或条件和整个系统的生产率支持模糊推理系统的决策过程。基于条件的监测模块在工作站的当地数字双胞胎中的条件的监测模块预测了资产的条件，而通过商店地板的全球数字双胞胎评估和确保生产率。本文介绍了分散和综合决策的框架，以便重新调度网络 - 物理生产系统，以及工业4.0试点流程中提出的方法的验证和校对 - 概念。实验结果表明，所提出的框架能够检测制造过程的变化，并做出适当的决策来重新安排过程。

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