Optimising Delivery Routes Under Real-World Constraints: A Comparative Study of Ant Colony, Particle Swarm and Genetic Algorithms

Abstract

Effective logistics systems are essential for fast and economical package delivery, especially in urban areas. The intricate and ever-changing nature of urban logistics makes traditional methods insufficient. Hence, requirements for the application of sophisticated optimisation techniques have increased. To optimise package delivery routes, this study compares the performance of three popular evolutionary algorithms: ant colony optimisation (ACO), particle swarm Optimisation (PSO), and genetic algorithms (GA). Finding the best algorithm to minimise delivery time and cost while taking into account real-world limitations, such as delivery priority. This guarantees that deliveries with a higher priority are prioritised over others, which may substantially impact route optimisation. We examine each algorithm to create the best possible route plans for delivery trucks using actual data. Several factors are employed to assess each algorithm's performance, including robustness to changes in environmental variables and computational efficiency—the simulation models delivery demands using actual data. Results indicate that ACO performed better in Los Angeles and Chicago, completing the shortest routes with respective distances of 126,254.18 and 59,214.68, indicating a high degree of flexibility in intricate urban layouts. With the best distance of 48,403.1 in New York, on the other hand, GA achieve good results, demonstrating its usefulness in crowded urban settings. These results highlight how incorporating evolutionary algorithms into urban logistics can improve sustainability and efficiency.