| This article presents a comprehensive study of various optimization algorithms applied to the Vehicle Routing Problem with Time Windows (VRPTW). The research focuses on comparing five different metaheuristic approaches: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), a Hybrid Algorithm combining SA and GA, and a Memetic Algorithm. The algorithms were implemented and tested on real-world routing scenarios with time window constraints, delivery and pickup demands, and multiple vehicles. The results demonstrate the effectiveness of the hybrid approach, which combines the global search capabilities of genetic algorithms with the local optimization strengths of simulated annealing. The research provides valuable insights into the practical application of these algorithms in solving complex vehicle routing problems, with particular attention to real-world constraints such as time windows, vehicle capacities, and environmental factors like fuel consumption and $CO_2$ emissions. |
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