Open Access

Andreas Löbel

• In this paper, we present a Dantzig-Wolfe decomposition for the $NP$-hard multiple-depot vehicle scheduling problem in public mass transit. It turned out that such a decomposition approach is an unsuitable method to solve such kind of multicommodity flow problems. The major obstacle to solve such problems is that the continuous master problem relaxations become too hard to be solved efficiently. Especially for problems with more than one thousand timetabled trips, the LU factorization in solving a restricted master problem takes far too much time. We will describe our computational experiments in detail and discuss the reasons why the decomposition method fails in this case. Our computational investigations are based on real-world problems from the city of Hamburg with up to 2,283 timetabled trips. Our decomposition implementation is compared with a delayed column generation to solve the linear programming (LP) relaxation directly. This LP method can solve the LP relaxations of the integer linear programming formulation exactly for truly large-scale real-world problems of the cities of Berlin and Hamburg.