Efficient primal heuristics for mixed-integer linear programs

TL;DR

This paper presents customized primal heuristics for mixed-integer linear programs, developed for a NeurIPS 2021 competition, demonstrating superior performance in finding high-quality feasible solutions.

Contribution

It introduces novel, dataset-specific primal heuristics tailored for mixed-integer linear programs, advancing solution efficiency in combinatorial optimization.

Findings

Proposed heuristics outperform competitors in computational studies.

Customized methods effectively identify high-quality feasible solutions.

Approaches are tailored for each dataset in the NeurIPS 2021 competition.

Abstract

This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in the competition, we propose customized primal heuristic methods to efficiently identify high-quality feasible solutions. The computational studies demonstrate the superiority of our proposed approaches over the competitors'.