  Recently, hybrid metaheuristics have become a trend in operations research. A
successful example combines the Greedy Randomized Adaptive Search Procedures
(GRASP) and data mining techniques, where frequent patterns found in
high-quality solutions can lead to an efficient exploration of the search
space, along with a significant reduction of computational time. In this work,
a GRASP-based state-of-the-art heuristic for the Minimum Latency Problem (MLP)
is improved by means of data mining techniques for two MLP variants.
Computational experiments showed that the approaches with data mining were able
to match or improve the solution quality for a large number of instances,
together with a substantial reduction of running time. In addition, 88 new cost
values of solutions are introduced into the literature. To support our results,
tests of statistical significance, impact of using mined patterns, equal time
comparisons and time-to-target plots are provided.
