Marin Lujak

IMT Lille Douai, France

Research question, scientific approach, and application

Distributed optimization and decision-making for large and complex systems

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 mostly the paradigm of multi-agent systems (MAS), combinatorial optimization, and artificial intelligence, where each (human, software, infrastructure, or robot) actor is a software 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. My research focus is on engineering distributed and scalable decision-making systems where a large decision problem should be decomposed into smaller and interconnected subproblems.†

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

Among many open issues, there is the one related with the ways to model the decision-making for relevant decision makers composing the system and the means to orchestrate and scale them considering a desired system behavior and social welfare. There is also the question of incentives: how to incentivize participating actors to create in them the sense of satisfaction when behaving collaboratively and not to exclusively rely on negative reinforcement and punishment measures such as penalties to realize systemís objectives.

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 multi-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 (COMRADES Project).