A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty
This work addresses operational planning problems requiring fast predictions, but it is incremental as it applies an existing method to a new domain with minimal adaptations.
The paper tackled predicting solutions to a stochastic discrete optimization problem under uncertainty by adapting a neural machine translation algorithm, achieving accurate results within milliseconds, though slightly less accurate than approximate stochastic programming solutions.
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.