In this paper, we address the problem of Column Generation (CG) using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to find the columns with most negative reduced cost in the Pricing Problem (PP). Unlike previous Machine Learning (ML) applications for CG, our model deploys an end-to-end mechanism as it independently solves the pricing problem without the help of any heuristic. We consider a variant of Vehicle Routing Problem (VRP) as a case study for our method. Through a set of experiments where our method is compared against a Dynamic Programming (DP)-based heuristic for solving the PP, we show that our method solves the linear relaxation up to a reasonable objective gap in significantly shorter running times.
@article{arxiv.2504.02383,
title = {Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing},
author = {Abdo Abouelrous and Laurens Bliek and Adriana F. Gabor and Yaoxin Wu and Yingqian Zhang},
journal= {arXiv preprint arXiv:2504.02383},
year = {2025}
}