Algorithmic Modelling and Optimization

Algorithmic Modelling and Optimization

The idea behind good planning is to do `more with less’, thereby improving the performance of the system. The logistic problems that we study either come from practice, or mimic situations that can be encountered in practice. Typical examples in this respect are synchronization and integrated decision making by decomposition. To solve such logistic problems, we design innovative algorithms and solution methods: the goal is to push the boundary of what is computable as far as possible to make the models as useful in practice as possible. We have extensive experience in the development of (integer) linear programming, column generation, and local search. Areas of application are public transport, rostering, power networks, and machine scheduling.