The vehicle routing problem is a classical problem in operations research, where the objective is to design least cost
routes for a fleet of identical capacitated vehicles to service geographically scattered customers. In this paper, we
present a new and effective hybrid metaheuristic algorithm for large-scale vehicle routing problem. The algorithm
combines the strengths of the well-known Nearest Neighbor Search and Tabu Search into a two-stage procedure.
More precisely, Nearest Neighbor Search is used to construct initial routes in the first stage and the Tabu Search is
utilized to optimize the intra-route and the inter-route in the second stage. The presented algorithm is specifically
designed for large-scale problems. The computational experiments were carried out on a standard benchmark and a
real dataset with 6772 tobacco customers. The results demonstrate that the suggested method is highly competitive.