Integrating Machine Learning Into Vehicle Routing Problem: Methods and Applications

Abstract

The vehicle routing problem (VRP) and its variants have been intensively studied by the operational research community. The existing surveys and the majority of the published articles tackle traditional solutions, including exact methods, heuristics, and meta-heuristics. Recently, machine learning (ML)-based methods have been applied to a variety of combinatorial optimization problems, specifically VRPs. The strong trend of using ML in VRPs and the gap in the literature motivated us to review the state-of-the-art. To provide a clear understanding of the ML-VRP landscape, we categorize the related studies based on their applications/constraints and technical details. We mainly focus on reinforcement learning (RL)-based approaches because of their importance in the literature, while we also address non RL-based methods. We cover both theoretical and practical aspects by clearly addressing the existing trends, research gap, and limitations and advantages of ML-based methods. We also discuss some of the potential future research directions.

Keywords

• Vehicle routing problem (VRP)
• machine learning
• reinforcement learning
• deep learning
• combinatorial optimization