Title: Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information,
Abstract: This paper offers a methodological contribution at the intersection of
machine learning and operations research. Namely, we propose a methodology to
quickly predict tactical solutions to a given operational problem. In this
context, the tactical solution is less detailed than the operational one but it
has to be computed in very short time and under imperfect information. The
problem is of importance in various applications where tactical and operational
planning problems are interrelated and information about the operational
problem is revealed over time. This is for instance the case in certain
capacity planning and demand management systems.
We formulate the problem as a two-stage optimal prediction stochastic program
whose solution we predict with a supervised machine learning algorithm. The
training data set consists of a large number of deterministic (second stage)
problems generated by controlled probabilistic sampling. The labels are
computed based on solutions to the deterministic problems (solved independently
and offline) employing appropriate aggregation and subselection methods to
address uncertainty. Results on our motivating application in load planning for
rail transportation show that deep learning algorithms produce highly accurate
predictions in very short computing time (milliseconds or less). The prediction
accuracy is comparable to solutions computed by sample average approximation of
the stochastic program.