Goal-oriented service composition for smart manufacturing

The rapid advancement of modern manufacturing systems, driven by Industry 4.0, has intensified the need for automated, adaptive, and resilient production planning. In this context, service composition has gained renewed interest as a key enabler of smart manufacturing. However, traditional service composition approaches often fail to address the dynamism and uncertainty typical of manufacturing environments, necessitating the development of more robust and flexible methodologies. This thesis revisits the concept of service composition, extending it beyond conventional frameworks to a goal-oriented paradigm, which ensures greater flexibility, robustness, and efficiency in orchestrating manufacturing processes. A fundamental contribution of this work is the seamless integration of Digital Twins within service composition frameworks developed, enabling real-time system monitoring and dynamic adaptation to evolving manufacturing conditions. The proposed methodologies are implemented and validated through available software libraries and real-world case studies, demonstrating their effectiveness in adaptive production planning and intelligent manufacturing control. By bridging the gap between formal service composition techniques and industrial applications, this research contributes to the development of scalable, efficient, and innovative automation solutions for smart manufacturing environments.