THE EFFICIENCY OF A GENETIC ALGORITHM FOR THE PROBLEM OF ROUTING CAPABLE VEHICLES

This work proposes the use of Genetic Algorithms (GA) to address the Enabled Vehicle Routing Problem (PRVC), which is a classic combinatorial optimization challenge. PRVC consists of determining the most efficient routes for a fleet of vehicles with limited capacity, in order to minimize the total distance traveled and meet the demands of a specific set of customers. Given the complexity of the problem, which is classified as NP-hard, meta-heuristic techniques, such as AG, are exploited to generate high-quality solutions in a feasible computational time. In this study, a genetic algorithm was developed and implemented with the aim of evaluating its effectiveness and potential to resolve PRVC instances. The methodology used involves the generation of initial populations, selection, crossing and mutation processes, seeking the continuous improvement of solutions over several generations.