A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty
Abstract
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 stateoftheart 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2019
 DOI:
 10.48550/arXiv.1910.08216
 arXiv:
 arXiv:1910.08216
 Bibcode:
 2019arXiv191008216F
 Keywords:

 Computer Science  Machine Learning;
 Statistics  Machine Learning