Learning for routing: A guided review of recent developments and future directions
Artikel i vetenskaplig tidskrift, 2025

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.

Traveling salesman problem

Routing problems

Vehicle routing problem

Reinforcement learning

Combinatorial optimization

Machine learning

Författare

Fangting Zhou

Chalmers, Elektroteknik, System- och reglerteknik

Attila Lischka

Chalmers, Elektroteknik, System- och reglerteknik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Jiaming Wu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Morteza Haghir Chehreghani

Data Science och AI 2

Göteborgs universitet

G. Laporte

University of Bath

HEC Montréal

Transportation Research Part E: Logistics and Transportation Review

1366-5545 (ISSN)

Vol. 202 104278

ERGODIC: Kombinerade person- och godstransporter i förortstrafik

Europeiska kommissionen (EU) (F-DUT-2022-0078), 2023-10-01 -- 2026-09-30.

VINNOVA (ERGODIC), 2023-10-01 -- 2026-09-30.

Europeiska kommissionen (EU) (F-ENUAC-2022-0003), 2023-10-01 -- 2026-09-30.

Ämneskategorier (SSIF 2025)

Produktionsteknik, arbetsvetenskap och ergonomi

Transportteknik och logistik

Datavetenskap (datalogi)

Styrkeområden

Transport

DOI

10.1016/j.tre.2025.104278

Mer information

Senast uppdaterat

2025-07-17