  Hybrid variations of metaheuristics that include data mining strategies have
been utilized to solve a variety of combinatorial optimization problems, with
superior and encouraging results. Previous hybrid strategies applied mined
patterns to guide the construction of initial solutions, leading to more
effective exploration of the solution space. Solving a combinatorial
optimization problem is usually a hard task because its solution space grows
exponentially with its size. Therefore, problem size reduction is also a useful
strategy in this context, especially in the case of large-scale problems. In
this paper, we build upon these ideas by presenting an approach named
MineReduce, which uses mined patterns to perform problem size reduction. We
present an application of MineReduce to improve a heuristic for the
heterogeneous fleet vehicle routing problem. The results obtained in
computational experiments show that this proposed heuristic demonstrates
superior performance compared to the original heuristic and other
state-of-the-art heuristics, achieving better solution costs with shorter run
times.
