Análise entre algoritmo genético e otimização por colônia de formigas aplicados ao problema de roteamento de veículos capacitados

Abstract

The modern world demands increasing operational efficiency to ensure products are delivered quickly and with quality, providing a superior customer experience. In this context, route optimization represents a central challenge for companies in the logistics sector. The Capacitated Vehicle Routing Problem (CVRP) stands out as one of the most relevant variants, requiring solutions that minimize operational costs while respecting capacity constraints. This work presents a comparative analysis between the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) applied to the CVRP. The algorithms were implemented in Kotlin, and tests were conducted on three standard benchmark instances from the literature, evaluating criteria such as solution quality (total distance and percentage deviation from the known optimum), execution time, statistical stability, and convergence behavior. The results showed that GA performed better on medium-sized instances, while ACO stood out on larger instances due to its robustness and lower variation between runs. This study contributes to the comparative understanding of these metaheuristics and provides insights for their application to real-world logistics problems.