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Transformation of semantic knowledge into simulation-based decision support
تحول دانش معنایی به پشتیبانی تصمیم گیری مبتنی بر شبیه سازی-2021 Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, indepth 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.
keywords: تحول دانش | پشتیبانی تصمیم | هستی شناسی | مدل سازی ترکیبی | شبیه سازی همه گیر | شبیه سازی زنجیره تامین | Knowledge Transformation | Decision Support | Ontologies | Hybrid modeling | Pandemic Simulation | Supply chain simulation |
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