Abstract
The following work presents a resolution of the Vehicle Routing
Problem, focusing on fuel distribution, using Evolutionary Algorithms,
which is a bio-inspired Artificial Intelligence technique. The
study examines the behavior of a monoobjective version compared
to the ‘MultiObjective Evolutionary Algorithm with Many Tables’
(AEMMT, in portuguese), which operates simultaneously with different
subpopulations, where each one is dedicated to minimizing a
specific objective function. Therefore, the objective is to determine
which of the two produces the best results when considering several
factors such as distance, time and fuel. As a result, in this work the
version algorithm with many objectives proved to be superior to the
single-objective version in relation to the value of the improvement
brought, especially analyzing the evolutionary behavior of each
criterion.