  The efficiency of science observation Short-Term Scheduling (STS) can be
defined as being a function of how many highly ranked observations are
completed per unit time. Current STS at ESO's Paranal observatory is achieved
through filtering and ranking observations via well-defined algorithms, leading
to a proposed observation at time t. This Paranal STS model has been
successfully employed for more than a decade. Here, we summarise the current
VLT(I) STS model, and outline ongoing efforts of optimising the scientific
return of both the VLT(I) and future ELT. We describe the STS simulator we have
built that enables us to evaluate how changes in model assumptions affect STS
effectiveness. Such changes include: using short-term predictions of
atmospheric parameters instead of assuming their constant time evolution;
assessing how the ranking weights on different observation parameters can be
changed to optimise the scheduling; changing STS to be more `dynamic' to
consider medium-term scheduling constraints. We present specific results
comparing how machine learning predictions of the seeing can improve STS
efficiency when compared to the current model of using the last 10\,min median
of the measured seeing for observation selection.
