Prof. Dr. Kevin Tierney

Learning to optimize

Why write methods to solve optimization problems yourself when a machine learned model can do it for you? We use machine learning techniques, such as deep reinforcement learning, to automatically learn heuristics for solving difficult optimization problems, such as routing and scheduling problems.

Optimization under uncertainty

The future is not certain, and this must be taken into account when planning decisions for the future. We are developing novel techniques to better incorporate uncertainty into optimization models and methods, with the goal of ensuring effective decisions regardless of what the future holds.

Efficient logistics and transportation

My group specializes in modeling and solving optimization problems in the areas of logistics and transportation, with a particular focus on maritime transportation. We use a wide range of techniques, ranging from mixed-integer linear programming, to constraint programming and metaheuristics. In this line of research, we work closely with industrial partners and have gained considerable expertise in developing solutions to real-world problems.