# MineReduce: an approach based on data mining for problem size reduction

arXiv: https://arxiv.org/abs/2005.07415 · HTML: https://mlsanity.com/paper/2005.07415/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 2 of 2)

- [Study of Robust Features in Formulating Guidance for Heuristic Algorithms for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/2508.06129/) (2025)
- [An Enhanced Large Neighborhood Search Approach for the Capacitated Facility Location Problem with Incompatible Customers](https://mlsanity.com/paper/2605.28337/) (2026)

## Abstract (arXiv)

Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.