The student William Steven Ochoa Agurto obtained an EXCELLENT CUM LAUDE grade
The student William Steven Ochoa Agurto obtained an EXCELLENT CUM LAUDE grade
The student William Steven Ochoa Agurto obtained an EXCELLENT CUM LAUDE grade
- Thesis title: Enhancing Flexibility in Industry 4.0 Workflows: A Context-Aware Architecture for Dynamic Service Orchestration
Court:
- Presidency: Pal Varga (Budapest University of Technology and Economics)
- Vocal: Saadia Dhouib (CEA)
- Vocal: Aintzane Armentia Diaz de Tuesta (UPV/EHU)
- Vocal: Aitor Aguirre Andueza (Ikerlan S.Coop.)
- Secretary: Miren Illarramendi Rezabal (Mondragon Unibertsitatea)
Abstract:
The Industry 4.0 era is reshaping the manufacturing landscape, fostering highly flexible processes that swiftly adapt to operational requirements, situational events, and environmental factors. To achieve this, it is crucial to orchestrate manufacturing operations while adhering to Industry 4.0 specifications. In pursuit of standardization, the Asset Administration Shell (AAS) emerges to provide a digital representation of physical assets. Concurrently, Workflow Management, with the Business Process Model and Notation (BPMN) language, offers an approach for coordinating tasks using specialized tools.
However, achieving flexible and adaptable workflows remains a challenge. Systems with context-awareness capabilities interpret real-time data and reconfigure themselves to minimize delays and improve overall efficiency. Addressing this challenge, the Semantic Web emerges as an approach to translate raw data into semantically enriched information, enabling intelligent decisions based on real-time contextual information.
This doctoral thesis presents an industry 4.0 architecture for context-aware workflow management that leverages recent advances in the state-of-the-art and addresses existing industry-related challenges. The architecture incorporates decoupled components that: 1) Integrate AAS for asset representation, BPMN for workflow notation, and a service discovery mechanism, offering a flexible tool for manufacturing process design. 2) Operate on central servers and edge-embedded systems with minimal resource consumption. And, 3) Provide a context-aware component for runtime workflow reconfiguration, enhancing the dynamism of manufacturing operations.
Furthermore, the architecture undergoes validation through several experiments, demonstrating effective context-awareness in dynamic service re-selection during workflow execution. Results show improvements in task completion time, task completion rate, and energy utilization. Additionally, this thesis highlights the flexibility of the architecture by implementing it in diverse manufacturing scenarios without altering individual components or changing the semantic repository structure. This work establishes a foundation for efficient context-aware workflow management in Industry 4.0, offering a versatile solution for todays manufacturing challenges.