  Due to increasing railway use, the capacity at railway yards and maintenance
locations is becoming limiting. Therefore, the scheduling of rolling stock
maintenance and the choice regarding optimal locations to perform maintenance
is increasingly complicated. This research introduces a Maintenance Scheduling
and Location Choice Problem (MSLCP). It simultaneously determines maintenance
locations and maintenance schedules of rolling stock, while it also considers
the available capacity of maintenance locations, measured in the number of
available teams. To solve the MSLCP, an optimization framework based on
Logic-Based Benders' Decomposition (LBBD) is proposed by combining two models,
the Maintenance Location Choice Problem (MLCP) and the Activity Planning
Problem (APP), to assess the capacity of a MLCP solution. Within the LBBD, four
cut generation procedures are introduced to improve the computational
performance: a naive procedure, two heuristic procedures and the so-called
min-cut procedure that aims to exploit the specific characteristics of the
problem at hand. The framework is demonstrated on a realistic scenarios from
the Dutch railways. It is shown that the best choice for cut generation
procedure depends on the objective: when aiming to find a good but not
necessarily optimal solution, the min-cut procedure performs best, whereas when
aiming for the optimal solution, one of the heuristic procedures is the
preferred option. The techniques used in the current research are new to the
current field and offer interesting next research opportunities.
