Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.

仿真能够应对工业价值链的不确定性和动态性。但是，对于将对象和约束从现实世界映射到虚拟模型，不可避免地需要深入的系统专业知识。这种知识密集性导致各个项目的开发时间长，这与及时的决策支持的需求相矛盾。由于越来越多的公司使用行业知识图和本体来促进他们的知识管理，因此本文提出了一个框架，该框架说明了如何从此类语义知识库中有效地得出仿真模型。作为该方法的一部分，新颖的Simulation Ontology为混合仿真提供了标准化的元模型。它的实例化使用户能够提出一个完全参数化的形式仿真模型。最新开发的映射规则通过提供有关如何将现有本体中的知识转化为仿真本体实例的指导，从而简化了此过程。该框架由解析过程完成，该解析过程用于将该概念模型自动转换为可执行模型。这种新颖的建模方法通过降低模型的复杂性使模型开发更加有效。它已在半导体制造的用例实现中得到验证，其中需要跨域知识才能建模和模拟COVID-19大流行对全球供应链网络的影响。该框架由解析过程完成，该解析过程用于将该概念模型自动转换为可执行模型。这种新颖的建模方法通过降低模型的复杂性使模型开发更加有效。它已在半导体制造的用例实现中得到验证，其中需要跨域知识才能建模和模拟COVID-19大流行对全球供应链网络的影响。该框架由解析过程完成，该解析过程用于将该概念模型自动转换为可执行模型。这种新颖的建模方法通过降低模型的复杂性使模型开发更加有效。它已在半导体制造的用例实现中得到验证，其中需要跨域知识才能建模和模拟COVID-19大流行对全球供应链网络的影响。