Workpackage 3 - Data-centric algorithm design
When we are faced with an optimisation problem there are two main alternatives for finding solutions. We can either develop an optimisation algorithm for finding a provably best solution, or we can settle for a high-quality solution that is obtained fast through an approximation algorithm. For optimisation algorithms we will investigate how machine learning can be used to guide the search to an optimal solution. When approximating, it is a challenge to derive algorithms that are not only guaranteed to perform well in the worst-case sense, as is mainly done today, but more interestingly for data that actually occur. We will develop new theoretical concepts for a beyond-worst-case analysis that incorporates data. In addition, we will develop algorithms that have a guaranteed performance according to the developed theory.
Workpackage 4 - Data-centric modeling
Traditionally, an optimisation model is built manually, but this is not always possible as some restrictions do not easily translate into mathematical functions. We will investigate how to use machine learning to generate a (part of the) model for a given problem based on the available data. Hence, this results in optimisation models that contain, e.g., a deep learning model or a random forest. The machine learning model is added as a constraint to the manually developed model. In this work package we will analyse which machine learning techniques can best be used, and how to solve optimisation models that also contain non-linear functions that result from machine learning.