Knowledge-guided local search for the vehicle routing problem

Local search has been established as a successful cornerstone to tackle the Vehicle Routing Problem, and is included in many state-of-the-art heuristics. In this paper we aim to demonstrate that a well implemented local search on its own suffices to create a heuristic that computes high-quality solutions in a short time. To this end we combine three powerful local search techniques, and implement them in an efficient way that minimizes computational effort. We conduct a series of experiments to determine how local search can be effectively combined with perturbation and pruning, and make use of problem specific knowledge, to guide the search to promising solutions more effectively. The heuristic created in this way not only performs well on many benchmark sets, it is also straightforward in its design and does not contain any components of which the contribution is unclear. (C) 2019 Elsevier Ltd. All rights reserved.