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arXiv · 2504.02383

Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing

Abdo Abouelrous·Laurens Bliek·Adriana F. Gabor·Yaoxin Wu·Yingqian Zhang
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Abstract

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.

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Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor, Yaoxin Wu, Yingqian Zhang. 2025-04-03. Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing. https://arxiv.org/abs/2504.02383

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