  This study is concerned with the determination of optimal appointment times
for a sequence of jobs with uncertain duration. We investigate the data-driven
Appointment Scheduling Problem (ASP) when one has $n$ observations of $p$
features (covariates) related to the jobs as well as historical data. We
formulate ASP as an Integrated Estimation and Optimization problem using a
task-based loss function. We justify the use of contexts by showing that not
including the them yields to inconsistent decisions, which translates to
sub-optimal appointments. We validate our approach through two numerical
experiments.
