Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining

TL;DR

This paper enhances a leading heuristic for the Minimum Latency Problem by integrating data mining techniques, resulting in improved solution quality and reduced computational time across multiple problem instances.

Contribution

It introduces a novel combination of GRASP and data mining for MLP, achieving better solutions faster and adding new benchmark solution costs to the literature.

Findings

Solution quality matched or improved in many instances

Significant reduction in computational time

88 new solution cost values introduced

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 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.