PhD defense Daniel Guzman Vargas
(29-08-2025) Pleased to announce that our researcher and business developer Daniel Guzman Vargas has succesfully publicly defended his doctoral dissertation on “Towards a Digital Twin Framework for Integrated Production Planning and Scheduling in Reconfigurable Manufacturing Systems."
Congratulations to our Dr. Daniel Guzman Vargas for succesfully defending his doctorate on “Towards a Digital Twin Framework for Integrated Production Planning and Scheduling in Reconfigurable Manufacturing Systems" on Thursday 28-08-2025 at iGent Zwijnaarde, at the Faculty of Engineering and Architecture.
Daniel also focussed on the Flanders Make Infraflex research infrastructure for flexible assembly, drone manufacturing and its production parameters, and the "Infraflex Digital Twin for Integrated Production Planning & Scheduling" which he has thoroughly worked on.
The jury consisted of supervisor our prof. Sidharta Gautama and the examination board members prof. Dries Benoit of the Department of Marketing, Innovation and Organisation, our prof. Dieter Claeys, our prof. Johannes Cottyn and prof. Kim Phuc Tran of Université de Lille, France. The ceremony was presided by Luc Dupre of the Department of Electromechanical, Systems and Metal Engineering.
The defense and ceremony were followed by a lovely reception.
Check out the LinkedIn post with more pictures right here.
You are welcome to read the adapted abstract of the doctoral dissertation below, as well.
Daniel's doctoral research addresses this critical gap by proposing a Digital Twin (DT) framework designed to enable responsive, integrated decision-making for RMSs. The core of his work is the development of a novel Responsive Decision-Making Support (RDMS) framework, which is designed to provide fast and efficient solutions to the Integrated Production Planning and Scheduling (IPPS) problem in real-world industrial settings.
Rather than relying on slow conventional optimization, the proposed method uses fast-to-evaluate surrogate models to predict the performance of optimal plans and schedules. This approach allows for the rapid evaluation of numerous scenarios, identifying high-quality, integrated solutions that enhance system agility and responsiveness.
Ultimately, his work provides a pathway toward more agile, efficient, and resilient manufacturing operations, bridging the gap between advanced optimization theory and the practical needs of smart factories in the Industry 4.0 era.
Daniel also focussed on the Flanders Make Infraflex research infrastructure for flexible assembly, drone manufacturing and its production parameters, and the "Infraflex Digital Twin for Integrated Production Planning & Scheduling" which he has thoroughly worked on.
The jury consisted of supervisor our prof. Sidharta Gautama and the examination board members prof. Dries Benoit of the Department of Marketing, Innovation and Organisation, our prof. Dieter Claeys, our prof. Johannes Cottyn and prof. Kim Phuc Tran of Université de Lille, France. The ceremony was presided by Luc Dupre of the Department of Electromechanical, Systems and Metal Engineering.
The defense and ceremony were followed by a lovely reception.
Check out the LinkedIn post with more pictures right here.
You are welcome to read the adapted abstract of the doctoral dissertation below, as well.
Towards a Digital Twin Framework for Integrated Production Planning and Scheduling in Reconfigurable Manufacturing Systems
Modern manufacturing companies are faced with increasing competition, market volatility, and a growing demand for customized products. Reconfigurable Manufacturing Systems (RMSs) have emerged to provide the necessary flexibility to meet these challenges. However, effectively managing these complex systems requires an integrated approach to production planning and scheduling, something traditional methods often fail to provide in the timely manner required by Industry 4.0.Daniel's doctoral research addresses this critical gap by proposing a Digital Twin (DT) framework designed to enable responsive, integrated decision-making for RMSs. The core of his work is the development of a novel Responsive Decision-Making Support (RDMS) framework, which is designed to provide fast and efficient solutions to the Integrated Production Planning and Scheduling (IPPS) problem in real-world industrial settings.
Rather than relying on slow conventional optimization, the proposed method uses fast-to-evaluate surrogate models to predict the performance of optimal plans and schedules. This approach allows for the rapid evaluation of numerous scenarios, identifying high-quality, integrated solutions that enhance system agility and responsiveness.
Ultimately, his work provides a pathway toward more agile, efficient, and resilient manufacturing operations, bridging the gap between advanced optimization theory and the practical needs of smart factories in the Industry 4.0 era.