  The parameters in Monte Carlo (MC) event generators are tuned on experimental
measurements by evaluating the goodness of fit between the data and the MC
predictions. The relative importance of each measurement is adjusted manually
in an often time-consuming, iterative process to meet different experimental
needs. In this work, we introduce several optimization formulations and
algorithms with new decision criteria for streamlining and automating this
process. These algorithms are designed for two formulations: bilevel
optimization and robust optimization. Both formulations are applied to the
datasets used in the ATLAS A14 tune and to the dedicated hadronization datasets
generated by the sherpa generator, respectively. The corresponding tuned
generator parameters are compared using three metrics. We compare the quality
of our automatic tunes to the published ATLAS A14 tune. Moreover, we analyze
the impact of a pre-processing step that excludes data that cannot be described
by the physics models used in the MC event generators.
