2021/02/19 by Ruibin Bai, Xinan Chen, Bai, Ruibin +31 · 3 citations
Computer Science · Engineering · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Transportation and Mobility Innovations #Urban and Freight Transport Logistics #Vehicle Routing Optimization Methods #cs.AI #cs.LG #math.OC
paper · pdf · doi:10.48550/arxiv.2102.10012
Submitted to International Journal of Production Research
arxiv created 2021/02/19 · openalex publication_date 2021/02/19 · arxiv updated 2021/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.