BENHIDA Soufia

Soufia Benhida is a researcher specializing in optimization, operations research, and data approximation methods, with affiliations to both INSA Rouen Normandie and ENSA Agadir. Her doctoral work focused on decision support systems for logistics chains under uncertainty, combining theoretical and applied approaches to problems like the Probabilistic Traveling Salesman Problem (PTSP). She collaborated on the M2NUM project (Normandy and Europe region) and received joint supervision from Ahmed Mir (ENSA Agadir), Christian Gout, and Arnaud Knippel (INSA Rouen).

• Ph.D. Defense: December 12, 2018
• Supervisors: Christian Gout, Ahmed Mir, Arnaud Knippel
• Projects: M2NUM, Logistics Optimization under Uncertainty

Her research integrates optimization techniques for solving complex problems in logistics and industrial engineering, with a focus on exact methods for the Traveling Salesman Problem and data approximation using finite elements and splines. Current work involves probabilistic modeling for market uncertainty applications and wind field approximation considering topographic factors.

Key publication trends include combinatorial optimization, subtour elimination constraints, and vector field approximation, reflecting interdisciplinary applications in logistics, environmental modeling, and industrial systems. Collaborations span institutions in Morocco and France.

She has presented her work at major conferences including the International Symposium on Combinatorial Optimization (Marrakech), CESCA Agadir, and LOGISTIQUA 2018, showcasing methodological comparisons and novel formulations for the TSP.