In this paper, we discuss the dynamic vehicle and crew scheduling
problem and we propose a solution approach consisting of solving a
sequence of optimization problems. Furthermore, we explain why it
is useful to consider such a dynamic approach and compare it with
a static one. Moreover, we perform a sensitivity analysis on our
main assumption that the travel times of the trips are known
exactly a certain amount of time before actual operation.
We provide extensive computational results on some real-world data
instances of a large public transport company in the Netherlands.
Due to the complexity of the vehicle and crew scheduling problem,
we solve only small and medium-sized instances with such a dynamic
approach. We show that the results are good in the case of a
single depot. However, in the multiple-depot case, the dynamic
approach does not perform so well. We investigate why this is the
case and conclude that the fact that the instance has to be split
in several smaller ones, has a negative effect on the performance
