A hybrid genetic algorithm for the vehicle routing problem with three-dimensional loading constraints

This paper addresses a Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP) which combines a three-dimensional loading problem and vehicle routing problem in distribution logistics. The problem requires the combinatorial optimization of a feasible loading solution and a successive routing of vehicles to satisfy client demands, where all vehicles must start and terminate at a central depot. In spite of its clear practical significance in the real world of distribution management, 3L-CVRP in literature is very limited for its high combinatorial complexity. We solve this problem by a hybrid approach which combines Genetic Algorithm and Tabu Search (GATS). Genetic algorithm is developed for vehicle routing and tabu search for three-dimensional loading, while these two algorithms are integrated for the combinatorial problem. We computationally evaluate this hybrid genetic algorithm on all publicly available test instances, and obtain new best solutions for several instances.