A.S. Pepin
http://repub.eur.nl/ppl/10803/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryA comparison of five heuristics for the multiple depot vehicle scheduling problem
http://repub.eur.nl/pub/18487/
Sun, 01 Feb 2009 00:00:01 GMT<div>A.S. Pepin</div><div>G. Desaulniers</div><div>A. Hertz</div><div>D. Huisman</div>
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.Comparison of heuristic approaches for the multiple depot vehicle scheduling problem
http://repub.eur.nl/pub/8069/
Tue, 07 Nov 2006 00:00:01 GMT<div>A.S. Pepin</div><div>G. Desaulniers</div><div>A. Hertz</div><div>D. Huisman</div>
Given a set of timetabled tasks, the multi-depot vehicle scheduling problem
is a well-known problem that 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 heuristic approaches for this problem,
namely, a heuristic \\mip solver, a Lagrangian heuristic, a column
generation heuristic, a large neighborhood search heuristic using column
generation for neighborhood evaluation, and a tabu search heuristic. The
first three methods are adaptations of existing methods, while the last two
are novel approaches for 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 a compromise between computational time and solution quality.