Combinatorial optimization

• Understand the fundamental concepts and specificities of combinatorial optimization (notions of complexity, combinatorial explosion, etc.)
• Identify and model issues from different fields or specifications in the form of combinatorial optimization problems
• Solve combinatorial problems using exact tree methods (branch-and-bound) to guarantee the optimality of the solutions obtained
• Design metaheuristic approximation methods (genetic algorithms, tabu search, etc.) to generate solutions adapted to the context, without guaranteeing optimality but with limited execution time
•  Implement the proposed algorithms and evaluate their performance on case studies from different fields: computer science, logistics, industrial engineering, etc.

This course focuses on modeling and approximate or exact solving of NP-hard combinatorial optimization and decision problems encountered in various fields.

linear programming