Solver based heuristics for rolling stocks corrective maintenance scheduling
Résumé
Passenger trains have a very precise schedule due to the transportation demand and
railway systems aim to exploit rolling stocks to their maximum capacity. Maintaining a
healthy network of rolling stocks can be really difficult because it must rely on an effec-
tive maintenance schedule that does not impact the transportation plan. But while some
maintenance operations are known beforehand, some repairing that could not have been
predicted still needs to be done. These jobs are brought to our knowledge through the train
itself. The time allowed to fix these malfunctions is relatively short (from a few hours to a
few days). It is allowed to schedule a complete repair, or a partial repair named diagnosis
that ensures that the train can be used in normal condition even if the operation is not
completely done. The aim of our study is to find an efficient way to schedule the starting
times of the maintenance jobs, completely or not, so that their due dates are met.
Section 2 defines the considered problem, next presents a Mixed Integer Linear Pro-
gramming (MILP) model and a Constraint Programming (CP) model. Section 3 introduces
two local search heuristics based on these models and Section 4 provides an overview of
the computational results.
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