  The local reconstruction of a railway schedule following a small perturbation
of the traffic, seeking minimization of the total accumulated delay, is a very
difficult and tightly constrained combinatorial problem. Notoriously enough,
the railway company's public image degrades proportionally to the amount of
daily delays, and the same goes for its profit! This paper describes an
inoculation procedure which greatly enhances an evolutionary algorithm for
train re-scheduling. The procedure consists in building the initial population
around a pre-computed solution based on problem-related information available
beforehand. The optimization is performed by adapting times of departure and
arrival, as well as allocation of tracks, for each train at each station. This
is achieved by a permutation-based evolutionary algorithm that relies on a
semi-greedy heuristic scheduler to gradually reconstruct the schedule by
inserting trains one after another. Experimental results are presented on
various instances of a large real-world case involving around 500 trains and
more than 1 million constraints. In terms of competition with commercial math
ematical programming tool ILOG CPLEX, it appears that within a large class of
instances, excluding trivial instances as well as too difficult ones, and with
very few exceptions, a clever initialization turns an encouraging failure into
a clear-cut success auguring of substantial financial savings.
