  We look at the problem of choosing a fleet of vehicles to carry out delivery
tasks across a very long time horizon -- up to one year. The delivery
quantities may vary markedly day to day and season to season, and the the
underlying routing problem may have rich constraints -- e.g., time windows,
compatibility constraints, and compartments. While many approaches to solve the
fleet size and mix (FSM) problem in various contexts have been described, they
usually only consider a "representative day" of demand. We present a method
that allows any such single-day FSM solver to be used to find efficient fleets
for a long time horizon.
  We propose a heuristic based on column generation. The method chooses a fleet
of vehicles to minimise a combination of acquisition and running costs over the
whole time horizon. This leads to fleet choices with much greater fleet
utilisation than methods that consider only a subset of the available demand
data. Issues with using a heuristic sub-problem solver within the context of
column generation are discussed.
  The method is compared to two alternative methods on real-world data, and has
shown to be very effective.
