Given a set of timetabled tasks, the multi-depot vehicle scheduling problem
is a well-known problem that consists of determining least-cost schedules
for vehicles assigned to several depots such that each task is accomplished
exactly once by a vehicle. In this paper, we propose to compare the
performance of five different heuristic approaches for this problem,
namely, a heuristic \\mip solver, a Lagrangian heuristic, a column
generation heuristic, a large neighborhood search heuristic using column
generation for neighborhood evaluation, and a tabu search heuristic. The
first three methods are adaptations of existing methods, while the last two
are novel approaches for this problem. Computational results on randomly
generated instances show that the column generation heuristic performs the
best when enough computational time is available and stability is required,
while the large neighborhood search method is the best alternative when
looking for a compromise between computational time and solution quality