Machine learning in manufacturing and logistics

Research topics

The team “Machine learning in manufacturing and logistics” focuses on modeling, simulating and optimising complex industrial and logistics systems.

We aim at improving performance (operational, environmental and economic) of industrial systems, by proposing a numerical approach, focused on the implementation of operational research, machine learning and data science. More precisely, scientific contributions in this domain are focused on the following topics:
• Supervised and unsupervised learning applied to sales forecasting;
• Optimisation of transportation decisions (vehicle routing);
• Reinforced learning applied to maintenance operations;
• Decision support systems for Supply Chain management using Machine Learning;
• Data mining and especially Social Media mining for marketing decisions;
• Industry 4.0 analytics and smart cities for enhanced logistics.


Researchers: Simon Tamayo, Frédéric Fontane, Arnaud de La Fortelle
PhD and research students: Arthur Gaudron, Apollinaire Barme, Augustin Lombard, Salma Benslimane, Angie Nguyen.

Ongoing projects

• Urban Logistics Research Chair: modelling, simulating and optimising urban freight;
• Data mining seasonal data – In collaboration with the Group Pomona;
• Machine learning and data visualisation for improving supply chain flows – In collaboration with Adents International.

Past recent projects