Enhancing a GRASP heuristic for the prize-collecting covering tour problem through data mining techniques

Glaubos Clímaco, Luidi Simonetti, Isabel Rosseti and Ítalo Santana

International Journal of Logistics Systems and Management, 2026, vol. 53, issue 1, 136-162

Abstract: Recent research has shown that hybrid heuristics, combining greedy randomised adaptive search procedures (GRASP) with data mining, are an effective approach to solving combinatorial optimisation problems. This paper presents a novel hybrid heuristic for the prize-collecting covering tour problem, which employs data mining techniques to enhance the GRASP algorithm. By leveraging patterns observed in high-quality solutions, our approach is able to explore the search space more efficiently, leading to improved results and reduced computational time. Our experimental results demonstrate the effectiveness of the proposed approach, which consistently outperforms existing methods across a wide range of problem instances. We present statistical significance tests, as well as an analysis of the impact of pattern mining and time-to-target plots, to support our findings.

Keywords: data mining; hybrid heuristics; greedy randomised adaptive search procedures; GRASP; prize-collecting covering tour problem; PCCTP. (search for similar items in EconPapers)
Date: 2026
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