A Column Generation-Based Approach for Railway Crew Scheduling Problem

In this paper, we describe the railway crew scheduling problem as a set covering model. Due to the large size of the problem and its intrinsic complexity, we propose an approach that starts with a decomposition phase to assign all trips to depots and gets a set of subproblems. A column generation algorithm is embed in genetic algorithm to get the lower bounds of the subproblems. In the second phase, we solve the linear programming relaxation of the models using a column generation scheme. Then we use a branch-and-bound technique to get satisfied integer solutions. The performance of this appraoch is compared with central solution by an instance. And our column geration based two-phase approach shows an improvement with respect to computation time.