In this area, we delve into the practical application of artificial intelligence (AI) in the manufacturing field. We aim to create new, innovative solutions that can optimize different aspects of manufacturing, such as efficiency, productivity, and sustainability. This research aligns with the ideals of Industry N.0, a trend towards automation and data exchange in manufacturing technologies.

We believe that collaboration between humans and AI can significantly improve the manufacturing sphere. Our research in this area is dedicated to the development and analysis of systems that facilitate this collaboration, with the end goals of enhancing user experiences, improving safety, and boosting productivity.

Our work here investigates the application of advanced control theory in managing complex manufacturing processes. The main focus is on creating systems that are not only resilient but also adaptable, capable of managing rapid changes in operational parameters.

In this research interest, we aim to strike a balance between automation and human control in various manufacturing scenarios. Our work involves studying the design and analysis of interactive systems that can successfully strike this balance.

Our research in this area involves creating models and simulations of discrete event time systems. The aim is to improve several aspects of manufacturing operations, including process scheduling, resource allocation, and overall operational efficiency.