ODERU: Optimisation of Semantic Service-Based Processes in Manufacturing

Luca Mazzola; Patrick Kapahnke; Matthias Klusch

In: Proc. 8th International Conference on Knowledge Engineering and Semantic Web. Knowledge Engineering and Semantic Web (KESW-17), LNCS, Springber, 2017.


A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring to react rapidly and eciently on the production capacities. Coupling it with the armed Service-Oriented Architectures (SOA) induces a need for agile collaboration among supply chain partners, but also between di erent divisions or branches of the same company. To this end, we propose a novel pragmatic approach for automatically implementing service-based manufacturing processes at design and run-time, called ODERU. It provides an optimal plan for a business process model, relying on a set of semantic annotations and a con gurable QoS-based constraint optimisation problem (COP) solving. The additional information encoding the optimal process service plan produced by means of pattern-based semantic composition and optimisation of non-functional aspects, are mapped back to the BPMN 2.0 standard formalism, through the use of extension elements, generating an enactable optimal plan. This paper presents the approach, the technical architecture and sketches two initial real-world industrial application in the manufacturing domains of metal press maintenance and automotive exhaust production.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence