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

A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty