A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

Logistics problems represent an important class of real-world problems where even small improvements in solution quality can lead to significant decreases in operational costs. However, these problems are usually NP-hard; thus, they are mostly solved using metaheuristic methods. To improve their performance, there is substantial research on crafting new and refined metaheuristics to derive superior solutions. This paper considers a truck routing problem within a naval port, where the objective is to minimize the total distance traveled by all the vehicles to distribute a given set of containers. Due to the large volume of goods that are being transferred through ports, it is imperative to improve the operation times at such ports to improve the throughput. To achieve this goal, a novel variable neighborhood search method that integrates a tabu list, an iterative local search procedure, and parallelization of neighborhood generation is proposed and evaluated. The experimental results demonstrate that the proposed method achieves similar results to the state of the art, but in a smaller amount of time.