Restricted Neighborhood Search for Large Scale Vehicle Routing Problems

Abstract

Vehicle routing problems (VRPs) are classical NP-hard problems. Those large scale and complex VRPs are even challenging. In this paper, we provide a general description of VRPs with heterogeneous constraints. For solving such type of VRPs in considerable solution quality and reasonable time, neighborhood search is a preferred choice. However, during the neighborhood search process, we may encounter a problem that the size of the neighboring solutions is still quite large, which consumes a great deal of computational resources. To tackle this problem, we propose a restricted neighborhood search method, which can ignore those non-promising neighboring solutions in a heuristic manner. Experiments on a real-world dataset show that our method can significantly accelerate the neighborhood search process, while the quality of the resultant solution is not impaired.