  Column generation is an iterative method used to solve a variety of
optimization problems. It decomposes the problem into two parts: a master
problem, and one or more pricing problems (PP). The total computing time taken
by the method is divided between these two parts. In routing or scheduling
applications, the problems are mostly defined on a network, and the PP is
usually an NP-hard shortest path problem with resource constraints. In this
work, we propose a new heuristic pricing algorithm based on machine learning.
By taking advantage of the data collected during previous executions, the
objective is to reduce the size of the network and accelerate the PP, keeping
only the arcs that have a high chance to be part of the linear relaxation
solution. The method has been applied to two specific problems: the vehicle and
crew scheduling problem in public transit and the vehicle routing problem with
time windows. Reductions in computational time of up to 40% can be obtained.
