  When optimizing problems with uncertain parameter values in a linear
objective, decision-focused learning enables end-to-end learning of these
values. We are interested in a stochastic scheduling problem, in which
processing times are uncertain, which brings uncertain values in the
constraints, and thus repair of an initial schedule may be needed. Historical
realizations of the stochastic processing times are available. We show how
existing decision-focused learning techniques based on stochastic smoothing can
be adapted to this scheduling problem. We include an extensive experimental
evaluation to investigate in which situations decision-focused learning
outperforms the state of the art for such situations: scenario-based stochastic
optimization.
