A set-partitioning-based heuristic for the vehicle routing problem

We develop a generic tabu search heuristic for solving the well-known vehicle routing problem. This algorithm explores the advantages of simple local search and improvement heuristics as well as a complex meta-heuristic. The solutions generated by these heuristics are selected and assembled by a set-partitioning model to produce superior solutions. Computational experience on standard benchmark problems is discussed and comparisons with other up-to-date heuristic methods are provided.