Research Topics - ROC
The research domains considered by the ROC Team (Operations Research, Combinatorial Optimization and Constraints) are branches of Operations Research and/or Artificial Intelligence (more specifically Constraint Programming). The ROC team proposes various models and algorithms for several classes of combinatorial optimization problems, such as scheduling, vehicle routing, resource allocation problems and more generally combinatorial problems in graphs. (more).
The team carries out research to establish structural properties and performance-guaranteed approximations of combinatorial optimization and other computing problems. This include complexity and approximability analysis, theoretical comparisons of linearization schemes, piecewise linear and polynomial approximations, polyhedral and graph theoretical studies (more).
The ROC team contributes on efficient solving of hard combinatorial optimization problem instances by designing contraint programming, mixed-integer linear programming and hybrid methods as well as by designing generic or specific solution methods (more).
The team is interested in the cooperative, decentralized and distributed aspects of decisions, related to the presence of several decision centers that interact in a number of applications. The team conducts research in multi-objective mathematical programming. Within multi-agent optimisation problems, the team is also exploring the search for equilibrium solutions within the meaning of game theory that are also non-Pareto dominated. Finally, the team is interested in distributed combinatorial optimization, especially for reasons of security or respect of private data (more).
The parameters of an optimization problem are often subject to uncertainties of all kinds. The team is interested in robust combinatorial optimization problems, especially scheduling problems under uncertainty. One avenue of research consists in proposing flexible solution structures for the proactive consideration of disruption by facilitating the repairing of computed solutions, notably by predetermining the feasibility of permutations within task sequences. The teams also contributes to advances in robust discrete optimization for scheduling (more).
The team explores the relationship between combinatorial optimization and learning techniques in two complementary ways. On the one hand, we seek to integrate learning mechanisms within tree search for problem solving. On the other hand, in a dual way, other work aims at improving machine learning techniques by integrating combinatorial optimization methods (more).
The ROC team aims at confronting the proposed methods to the real world by considering industrial engineering, human aspects andindustrial applications to various domains including transportation, manufacturing and supply chain management, energy management, aeronautics and space (more).