An Approach to Solve the Heterogeneous Fixed Fleet Vehicle Routing Problem With Time Window Based on Adaptive Large Neighborhood Search Meta-Heuristic

In the current economy, companies are increasingly interested in optimizing their logistics operations to reduce costs and increase agility. Transport logistics is one of the processes to be optimized, since companies have a limited fleet of heterogeneous vehicles, with particular capacities and costs, and have to attend to their customers within restricted periods. These features characterize the problem as a Heterogeneous Fixed Fleet Vehicle Routing Problem with Time Window (HFVRPTW). To solve this problem, this work proposes a method based on the Adaptive Large Neighborhood Search (ALNS) metaheuristic particularly focused on selecting vehicles that reduce the costs of the used fleet. The experiments showed that the proposed method improved 69.6% of the benchmark instances compared with the literature state-of-the-art, with 0.44% of average reduction in the total cost. Besides that, the implemented ALNS algorithm was around 35 times faster to run than the most relevant compared work.