Given a set of timetabled tasks, the multi-depot vehicle scheduling problem consists of determining least-cost schedules for vehicles assigned to several depots such that each task is accomplished exactly once by a vehicle. In this paper, we propose to compare the performance of five different heuristics for this well-known problem, namely, a truncated branch-and-cut method, a Lagrangian heuristic, a truncated column generation method, a large neighborhood search heuristic using truncated column generation for neighborhood evaluation, and a tabu search heuristic. The first three methods are adaptations of existing methods, while the last two are new in the context of this problem. Computational results on randomly generated instances show that the column generation heuristic performs the best when enough computational time is available and stability is required, while the large neighborhood search method is the best alternative when looking for good quality solutions in relatively fast computational times.

Additional Metadata
Keywords Branch-and-cut, Column generation, Computational methods, Heuristic algorithms, Heuristic methods, Heuristics, Lagrange multipliers, Lagrangian heuristic, Large neighborhood search, Multiple depot, Scheduling, Tabu search, Vehicle scheduling, Vehicles
Persistent URL dx.doi.org/10.1007/s10951-008-0072-x, hdl.handle.net/1765/18487
Citation
Pepin, A.S, Desaulniers, G, Hertz, A, & Huisman, D. (2009). A comparison of five heuristics for the multiple depot vehicle scheduling problem. Journal of Scheduling, 12(1), 17–30. doi:10.1007/s10951-008-0072-x