2018

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Larsen, Eric, Lachapelle, Sébastien, Bengio, Yoshua et al.

Understand

This paper offers a methodological contribution at the intersection of machine learning and operations research.

Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs).
The problem we address occurs in the context of two-stage stochastic programming where the second stage is demanding computationally.
We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.

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