Optimization problems in practice are often analyzed using computer models (e.g., stochastic
simulation models). These models can be expensive, though, in terms of computation time and/or
cost. To remedy this issue, metamodels can be used, which yield (data-driven) approximations of the
simulation model, while being computationally cheap(er) to evaluate. Machine Learning (ML)-based
optimization is a relatively novel, yet highly promising field within operations research. This project
particularly focuses on ML-based many-objective optimization of (noisy) conflicting objectives, using
preference learning. In many-objective settings, the decision-maker (DM) is typically only interested
in a subset of the Pareto optimal solutions; hence, incorporating these user preferences in the
optimization framework would not only allow the algorithm to converge to the relevant solutions
faster, but also to scale up to larger sets of objectives in a data-efficient manner. Learning these
underlying user preferences is challenging, though, as preference information is typically distorted by
noise (e.g., due to cognitive burden of the DM). The key research objectives of this project are
threefold: (i) to study the scalability issue in many-objective optimization; (ii) to develop a
methodology for modelling noisy preferences, and to use this information in an ML-based
optimization framework ; and (iii) to develop novel performance metrics for measuring the quality of
the obtained solutions