Enhanced Hub Location Routing with Branch-and-Cut Methods and Simplified Mathematical Models

Abstract

This article presents an advanced approach to the hub location routing problem, focusing on the optimal placement of hub nodes and the allocation of spoke nodes. We introduce a novel branch-and-cut method combined with a new simplified mathematical model incorporating valid inequalities. This hybrid approach aims to enhance solution quality and computational efficiency. Our proposed method integrates a learning mechanism to guide local searches, leveraging dual information from Lagrangian relaxation. Computational experiments validate the effectiveness of the proposed method.