Learning for routing: A guided review of recent developments and future directions

TL;DR

This paper reviews recent machine learning approaches to solve complex routing problems like TSP and VRP, emphasizing their integration with traditional methods and future research directions.

Contribution

It introduces a taxonomy of ML-based routing methods, categorizing them into construction and improvement approaches, and provides a structured framework for future research.

Findings

ML techniques enhance routing problem solutions

Taxonomy clarifies ML method applicability

Framework guides future research directions

Abstract

This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems, exact algorithms often require excessive computational time to find optimal solutions, while heuristics can only provide approximate solutions without guaranteeing optimality. With the recent success of machine learning models, there is a growing trend in proposing and implementing diverse ML techniques to enhance the resolution of these challenging routing problems. We propose a taxonomy categorizing ML-based routing methods into construction-based and improvement-based approaches, highlighting their applicability to various problem characteristics. This review aims to integrate traditional OR methods with state-of-the-art ML techniques, providing a structured framework to guide future research and address emerging VRP variants.