I am a postdoctoral researcher at the University of Twente, working on future-proof logistics systems, self-organisation, and AI-supported decision-making in transport and logistics networks. My research focuses on how autonomous actors, such as logistics companies or transport operators, can coordinate their decisions in a decentralised way while preserving privacy, trust, and operational feasibility.
My work lies at the intersection of transportation systems, logistics optimisation, artificial intelligence, multi-agent systems, and institutional design. I am particularly interested in how decentralised coordination mechanisms can help complex socio-technical systems become more efficient, resilient, and adaptive without relying only on centralised control.
Before and alongside my academic work, I have gained practical experience in railway operations, planning, and organisational change. This background strongly shapes my research perspective: I am interested not only in theoretically elegant models, but also in solutions that can be understood, trusted, and implemented in real operational environments.
Through my research, I aim to contribute to transport and logistics systems that are more collaborative, sustainable, and human-centred.
Organisations
Future Proof Smart Logistics
I participate in the Future Proof Smart Logistics project, led by the University of Twente and funded by TNO. The project investigates how the logistics sector can move from isolated, company-specific planning towards collaborative and connected logistics networks based on shared resources, decentralised decision-making, and trusted data exchange. It contributes to the broader vision of the Physical Internet (PI), aiming to improve the efficiency, sustainability, and resilience of logistics systems.
My contribution focuses on decentralised, privacy-preserving, and AI-supported coordination mechanisms for the self-organisation of collaborative logistics. I study how logistics actors can identify, evaluate, and commit to mutually beneficial collaboration opportunities without revealing sensitive operational information, such as schedules, route plans, private constraints, or cost functions. A central objective is to develop coordination mechanisms that remain robust under changing logistics demand, market conditions, and operational disruptions.
A key part of my work is the use of artificial intelligence and machine learning to support decision-making in complex logistics networks. This includes exploring how learning-based methods, such as graph-based machine learning and reinforcement learning, can help agents evaluate collaboration opportunities, estimate the value of exchanges, and improve coordination in dynamic and uncertain environments.
Through this work, I aim to contribute to logistics systems that are not only more efficient at the system level, but also acceptable, trustworthy, and feasible for the individual companies participating in collaboration.
Project Website
Address
University of Twente
Ravelijn (building no. 10), room 3426
Hallenweg 17
7522 NH Enschede
Netherlands
University of Twente
Ravelijn 3426
P.O. Box 217
7500 AE Enschede
Netherlands
Scan the QR code or