  Event generators play an important role in all physics programs at the Large
Hadron Collider and beyond. Dedicated efforts are required to tune the
parameters of event generators to accurately describe data. There are many
tuning methods ranging from expert-based manual tuning to surrogate
function-based semi-automatic tuning, to machine learning-based re-weighting.
Although they scale differently with the number of generator parameters and the
number of experimental observables, these methods are effective in finding
optimal generator parameters. However, none of these tuning methods includes
the Monte Carlo (MC) systematic uncertainties. That makes the tuning results
sensitive to systematic variations. In this work, we introduce a novel method
to incorporate the MC systematic uncertainties into the tuning procedure and to
quantitatively evaluate uncertainties associated with the tuned parameters.
Tested with a dummy example, the method results in a $\chi^2$ distribution that
is centered around one, the optimal generator parameters are closer to the true
parameters, and the estimated uncertainties are more accurate.
