In discrete optimization problems the progress of objects over time is
frequently modeled by shortest path problems in time expanded networks, but longer
timespansorfinertimediscretizationsquicklyleadtoproblemsizesthatareintractable
inpractice.Inconvexrelaxationsthearisingshortestpathsoftenlieinanarrowcorridor
inside these networks. Motivated by this observation, we develop a general dynamic
graph generation framework in order to control the networks’ sizes even for infinite
time horizons. It can be applied whenever objects need to be routed through a traffic or
production network with coupling capacity constraints and with a preference for early
paths. Without sacrificing any information compared to the full model, it includes a
few additional time steps on top of the latest arcs currently in use. This “frontier”
of the graphs can be extended automatically as required by solution processes such
as column generation or Lagrangian relaxation. The corresponding algorithm is effi-
ciently implementable and linear in the arcs of the non-time-expanded network with
a factor depending on the basic time offsets of these arcs. We give some bounds on
the required additional size in important special cases and illustrate the benefits of this
technique on real world instances of a large scale train timetabling problem