Machine Learning to Solve Vehicle Routing Problems: A Survey
Bogyrbayeva A. Meraliyev M. Mustakhov T. Dauletbayev B.
1 June 2024Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Intelligent Transportation Systems
2024#25Issue 64754 - 4772 pp.
This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both the machine learning and operations research communities in solving VRPs either through pure learning methods or by combining them with traditional handcrafted heuristics. We present a taxonomy of studies on learning paradigms, solution structures, underlying models, and algorithms. Detailed results of state-of-the-art methods are presented, demonstrating their competitiveness with traditional approaches. The survey highlights the advantages of the machine learning-based models that aim to exploit the symmetry of VRP solutions. The paper outlines future research directions to incorporate learning-based solutions to address the challenges of modern transportation systems.
neural combinatorial optimization , Reinforcement learning , supervised learning , vehicle routing
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SDU University, Department of Computer Science, Kaskelen, 040900, Kazakhstan
SDU University
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