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REALOPT Research team

Reformulations based algorithms for Combinatorial Optimization

Team presentation

Our aim is to develop tight formulations for combinatorial problems by combining the latest reformulation techniques, such as Lagrangian and polyhedral approach, non-linear programming tools and graph theoretics tools. Through industrial partnerships, the team targets large scale problems such as those arising in logistics (routing problems), in planning and scheduling, in network design and control, and in placement problems (cutting stock problems).

Research themes

Our project brings together complementary expertise in combinatorial optimization : Mixed Integer Programming (Polyhedral, Lagrangian and decomposition approaches, Branch-and-Price-and-Cut Algorithms), Quadratic programming (semi-definite-programming), and Graph Therory (for induced properties and implicit representation of solutions). We develop approximate solutions for large scale problems through mathematical programming based primal heuristics.

International and industrial relations

We have an associated team in Brazil through which we collaborate with Artur Pessoa and Eduardo Uchoa (Universidade Federal Fluminense) and Marcus Poggi (PUC-Rio)
Our Industrial partners are RTE, EDF and SNCF. We also had previous collaborations with Saint-Gobain Recherche, Ertus, Asys and Renault.

Keywords: Operations research Combinatorial optimization Graph Decomposition Branch-and-price Branch-and-cut Primal heuristics