Description

This thesis aims to develop a hybrid methodological framework that integrates a machine learning-based module with optimization methods relying on metaheuristic or matheuristic strategies. It addresses tactical and operational problems in fluvial and intermodal transportation, initially in a deterministic context and subsequently in a stochastic one, ultimately leading to an integrated tactical-operational framework under uncertainty.

Skills required

We are seeking candidates with a strong academic background and proven skills in: • Operations research, combinatorial optimization, applied mathematics, or data intelligence for decision support; • Proficiency in programming (C, C++, Python, Java, or C#); • A good level of English (written and spoken). An interest in machine learning applied to optimization would be highly appreciated, as would prior experience in transport planning involving optimization.

Bibliography

- Bengio, Yoshua, Andrea Lodi, et Antoine Prouvost. 2021. «Machine learning for combinatorial optimization: a methodological tour d’horizon.» European Journal of Operational Research 290(2), 405-421.
- Bilegan, Ioana, Teodor Gabriel Crainic, et Yunfei Wang. 2022. «Scheduled service network design with revenue management considerations and an intermodal barge transportation illustration.» European Journal of Operational Research 300(1), 164-177.
- Cui, Yaheng, Chenghao Wang, Ioana Bilegan, Eric Duchenne, et Walter Rei. 2025. «An Integrated Tactical-Operational Decision-Making Framework for Inland Waterway Transport under Navigability and Demand Uncertainties.» Euro Working Group on Transportation (EWGT) 27th Annual Conference, September.
- Cui, Yaheng, Ioana Bilegan, Eric Duchenne, et David Duvivier. 2024. «Demand rerouting mechanisms with revenue management for intermodal barge transportation networks.» Transportmetrica B: Transport Dynamics 12(1), 2416182.
- Wang, Chenghao, Ioana Bilegan, Walter Rei, et David Duvivier. 2024. «Addressing water level uncertainty for inland waterway transportation: a partially joint chance-constrained programming approach.» Odysseus, May.

Keywords

Operations research, Decision support system, Machine learning, Intelligent transportation