Research in the field of vehicle routing is often focused on finding new ideas and concepts in the development of fast and efficient algorithms for an improved solution process. Early studies introduce static tailor-made strategies, but trends show that algorithms with generic adaptive policies - which emerged in the past years - are more efficient to solve complex vehicle routing problems. | Yugoslav Journal of Operations Research 25 (2015), Number 2, 169–184 DOI: Invited survey ADAPTIVE SEARCH TECHNIQUES FOR PROBLEMS IN VEHICLE ROUTING, PART II: A NUMERICAL COMPARISON Stefanie KRITZINGER, Karl F. DOERNER Department of Production and Logistics, Johannes Kepler University Linz, Austria , Fabien TRICOIRE, Richard F. HARTL Department of Business Administration, University of Vienna, Austria , Received: January 2014 / Accepted: April 2014 Abstract: Research in the field of vehicle routing is often focused on finding new ideas and concepts in the development of fast and efficient algorithms for an improved solution process. Early studies introduce static tailor-made strategies, but trends show that algorithms with generic adaptive policies - which emerged in the past years - are more efficient to solve complex vehicle routing problems. In the first part of the survey, we presented an overview of recent literature dealing with adaptive or guided search techniques for problems in vehicle routing. Keywords: Adaptive Strategies, Local Search, Variable Neighborhood Search, Vehicle Routing. MSC: 90B06, 90C05, 90C08. 1. INTRODUCTION As it is shown in Part I of this survey [10], different adaptive mechanisms can be used when solving vehicle routing problems (VRPs) with metaheuristics. The survey started with basic local search-based methods, . adaptive tabu search or guided local search, followed by hybrid local search methods, . iterated local search (ILS), adaptive variable neighborhood search (AVNS), and adaptive 170 S. Kritzinger, F. Tricoire, K. F. Doerner, R. F. Hartl / Adaptive Search Techniques large neighborhood search (ALNS). The survey concluded with population-based methods, . ant colony optimization, memetic and genetic algorithms. In this second part, we evaluate and analyze different adaptive strategies on the .
