Operations Research, Combinatorial Optimization and Constraints

TEAM NEWS

26.04.24 PRESTO project wins IEA 2023 call - CNRS Sciences informatiques

HEAD

PHD DELEGATE

Team ROC works on combinatorial optimisation problems, and on the algorithmic methods to solve them, bridging Operations Research and Artificial Intelligence.

Our Research

Combinatorial Optimization Problems

Team ROC designs and studies various models and algorithms for several classes of combinatorial optimization problems, such as scheduling, vehicle routing, and resource allocation problems.

Computational Methods

We contribute to the design of efficient solving methods for hard combinatorial optimization problems. In particular, we design algorithms and models for constraint programming, mixed-integer linear programming and hybrid approaches.

Real-world Applications

We aim at confronting the proposed methods to the real world by considering industrial engineering, human factors and environmental issues. We develop industrial applications in various domains including transportation, manufacturing and supply chain management, energy management, aeronautics and space

Focus

Structural Properties and approximations with guarantees

We carry 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

Optimization under uncertainty

The parameters of an optimization problems 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

Combinatorial Optimization & Learning

We explore 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.

Multi-Agent & Multi-Objective Optimization

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. Interdisciplinary researches on human factors in combinatorial optimization have also been carried out.

Some public source codes and softwares from the group's research.