Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining
This work addresses optimization challenges in operations research for logistics or scheduling domains, but it is incremental as it builds on existing methods.
The paper tackled the Minimum Latency Problem by improving a state-of-the-art heuristic with data mining techniques, resulting in matching or improving solution quality for many instances and a substantial reduction in running time, including 88 new cost values.
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.