Machine Learning to Solve Vehicle Routing Problems: A Survey

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 combin...

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Vydané v:IEEE transactions on intelligent transportation systems Ročník 25; číslo 6; s. 4754 - 4772
Hlavní autori: Bogyrbayeva, Aigerim, Meraliyev, Meraryslan, Mustakhov, Taukekhan, Dauletbayev, Bissenbay
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Computational modeling
Logistics
Machine learning
neural combinatorial optimization
Reinforcement learning
Routing
supervised learning
Surveys
Taxonomy
Transportation
Transportation systems
Vehicle routing
ISSN:1524-9050, 1558-0016
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3334976