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

Fangting Zhou, Attila Lischka, Balázs Kulcsár, Jiaming Wu, Morteza Haghir Chehreghani and Gilbert Laporte

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 202, issue C

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.

Keywords: Machine learning; Reinforcement learning; Routing problems; Traveling salesman problem; Vehicle routing problem; Combinatorial optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104278

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