Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

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

Preface Guidelines for the use of meta-heuristics

The 18th EURO Summer/Winter Institute (ESWI XVIII) took place during the spring 2000 in Switzerland. The topic of ESWI XVIII, ‘‘Meta-heuristics in Combinatorial Optimization’’, was selected due to its great current scientific interest: indeed, in recent years, several meta-heuristics have proved to be highly efficient for the solution of difficult combinatorial optimization problems. The Institute was focused more particularly on the development and the use of local search and population search algorithms. Applications of these meta-heuristics on academic or real life problems were also discussed. This special issue of EJOR contains papers written by the participants to ESWI XVIII. These papers have benefited from fruitful discussions among the participants, the organizers and the invited speakers. We have tried to summarize here below some guidelines that should help in the design of successful adaptations of meta-heuristics to difficult combinatorial optimization problems