Marin Lujak

IMT Lille Douai, France


How to efficiently coordinate individual actors of large complex systems in a scalable and robust way with quality of solution guarantees while considering both individual interest and system performance?

To answer this question, I use the paradigm of multi-agent systems (MAS) and combinatorial optimization where each (human, software, infrastructure, or robot) actor is an agent making decisions independently and autonomously based on its local computations and the communication with others.

The challenge in the distribution and decentralization of decision making lies in the complexity of both the coordination problem at hand and a solution approach that should consider the balance between local computation and communication with others.

I develop mathematical programming models and algorithms that manage the systemís bottlenecks and decompose the coordination problem considering the constraints between individual and shared decisional variables.

The result is a distributed or decentralized decision making architecture that enables each agent to decide in its best interest considering a momentary context and system constraints.† These constraints are modelled to influence individual decisions such that given fairness and social welfare criteria are optimized.

The quality of solution strongly depends on the quality of available information and is based on sensory and communication technologies, whose developments give rise to new real-time agent coordination technologies applicable in various real-world industrial and business contexts.

My long term objective is to lower the inefficiency of the decision making paradigm based on Nash equilibrium through plausible MAS coordination solutions that will get closer to the system optimum while increasing fairness and social welfare.

I apply my research to resolving societal challenges including:

 Smart, Green and Integrated Transport and in more specific the development of distributed Route Guidance Systems (RGS) for the assignment of efficient, fair, and envy-free routes to users in a distributed way in real-time and the coordination of commercial fleets without the need for a dispatching center.

 Emergency Management: i) distributed coordination of Emergency Medical Systems with ambulances and ii) distributed coordination of evacuation routes in emergency evacuation of buildings, neighborhoods or cities.

 Multi-Robot Coordination: i) mobile industrial robots within robot teams working on a factory shop-floor and ii) teams of mobile service robots with humans for human assistance and support (newly obtained COMRADES Project).