Combining Preprocessing Approaches to Boost SR Performance

In this article, we delve into the world of vehicle routing problems (VRP) and explore the latest advancements in metaheuristic techniques. VRP is a complex logistical challenge that involves finding the most efficient routes for delivering goods or services to customers while taking into account various constraints such as time, cost, and sustainability.
Metaheuristics are high-level algorithms that provide flexible and efficient solutions to VRPs. They work by combining different techniques, such as simulation, local search, and machine learning, to explore the vast solution space of VRPs. In this review, we evaluate six state-of-the-art metaheuristics: Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Local Search (LS), and Neural Networks (NN).
To evaluate these algorithms, we analyzed their performance in various VRP instances. We used benchmark instances from the literature and tested each metaheuristic on each instance. Our results show that GA outperforms all other algorithms in most cases, particularly when the problem size increases. SA is a close second, followed by PSO and ACO. LS and NN perform relatively poorly but have potential for improvement through further optimization.
We also analyzed the impact of parameterization on the performance of these metaheuristics. Parameterization refers to the process of setting up the algorithm with specific parameters that determine its behavior and performance. Our findings indicate that proper parameter tuning is crucial for achieving good performance, and each algorithm has unique parameters that need to be adjusted for optimal results.
In conclusion, this review provides a comprehensive overview of state-of-the-art metaheuristics for solving VRPs. By examining their strengths, weaknesses, and performance in various instances, we can determine which algorithm is most suitable for specific problem instances. As VRPs continue to be an important area of research, these metaheuristics will play a crucial role in finding efficient and sustainable solutions for logistical challenges.