Abstract
Decision support systems in production and manufacturing help to achieve economic and ecological objectives and increase flexibility and resilience. Semantic models as knowledge base for these systems enable and empower analytical components such as machine learning, simulation, and optimization algorithms. This paper discusses some of the relevant aspects of such semantic models. Existing solutions and standards for common data models in manufacturing are explored, and based on that, alternatives and decisions for managing manufacturing operations are modeled in an ontology. Specifically, a general ontology for handling decisions in any type of factory is introduced.
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