  This paper is devoted to the prediction of solutions to a stochastic discrete
optimization problem. Through an application, we illustrate how we can use a
state-of-the-art neural machine translation (NMT) algorithm to predict the
solutions by defining appropriate vocabularies, syntaxes and constraints. We
attend to applications where the predictions need to be computed in very short
computing time -- in the order of milliseconds or less. The results show that
with minimal adaptations to the model architecture and hyperparameter tuning,
the NMT algorithm can produce accurate solutions within the computing time
budget. While these predictions are slightly less accurate than approximate
stochastic programming solutions (sample average approximation), they can be
computed faster and with less variability.
