Improving a state‐of‐the‐art heuristic for the minimum latency problem with data mining

Abstract

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 paper, a GRASP‐based state‐of‐the‐art heuristic for the minimum latency problem is improved by means of data mining techniques. Computational experiments showed that the hybrid heuristic with data mining was able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. Besides, 32 new best‐known solutions are introduced to the literature. To support our results, statistical significance tests, analyses over the impact of mined patterns, comparisons based on running time as stopping criterion, and time‐to‐target plots are provided.

Reactive GRASP for the Prize-collecting Covering Tour Problem

“…In order to improve R-GRASP performances on PCCT problems, we may reconsider the GENI procedure which represents the major part of the computational time. For the construction phase, we could avoid building the initial solution from scratch by using long-term memory, through data mining or path-relinking [28]. For improving the solution quality, we intend to study the incorporation of a MIP model to the R-GRASP for solving related subproblems of the PCCTP, since it has been proved to be a interesting approach [1,2,8].…”