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Analytics and machine learning in vehicle routing research.

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Title: Analytics and machine learning in vehicle routing research.
Authors: Bai, Ruibin1 (AUTHOR) ruibin.bai@nottingham.edu.cn, Chen, Xinan1 (AUTHOR), Chen, Zhi-Long2 (AUTHOR), Cui, Tianxiang1 (AUTHOR), Gong, Shuhui3 (AUTHOR), He, Wentao1 (AUTHOR), Jiang, Xiaoping4 (AUTHOR), Jin, Huan1 (AUTHOR), Jin, Jiahuan1 (AUTHOR), Kendall, Graham5,6 (AUTHOR), Li, Jiawei1 (AUTHOR), Lu, Zheng1 (AUTHOR), Ren, Jianfeng1 (AUTHOR), Weng, Paul7,8 (AUTHOR), Xue, Ning8 (AUTHOR), Zhang, Huayan1 (AUTHOR)
Source: International Journal of Production Research. Jan 2023, Vol. 61 Issue 1, p4-30. 27p. 1 Diagram, 3 Charts.
Subjects: Vehicle routing problem, Online algorithms, Problem solving, Machine learning
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Analytics and machine learning in vehicle routing research.
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  Data: <searchLink fieldCode="AR" term="%22Bai%2C+Ruibin%22">Bai, Ruibin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ruibin.bai@nottingham.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xinan%22">Chen, Xinan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhi-Long%22">Chen, Zhi-Long</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Tianxiang%22">Cui, Tianxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Shuhui%22">Gong, Shuhui</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Wentao%22">He, Wentao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Xiaoping%22">Jiang, Xiaoping</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Huan%22">Jin, Huan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Jiahuan%22">Jin, Jiahuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kendall%2C+Graham%22">Kendall, Graham</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiawei%22">Li, Jiawei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Zheng%22">Lu, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Jianfeng%22">Ren, Jianfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Weng%2C+Paul%22">Weng, Paul</searchLink><relatesTo>7,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Ning%22">Xue, Ning</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huayan%22">Zhang, Huayan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jan 2023, Vol. 61 Issue 1, p4-30. 27p. 1 Diagram, 3 Charts.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Vehicle+routing+problem%22">Vehicle routing problem</searchLink><br /><searchLink fieldCode="DE" term="%22Online+algorithms%22">Online algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207543.2021.2013566
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      – Code: eng
        Text: English
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        PageCount: 27
        StartPage: 4
    Subjects:
      – SubjectFull: Vehicle routing problem
        Type: general
      – SubjectFull: Online algorithms
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: Analytics and machine learning in vehicle routing research.
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            – D: 01
              M: 01
              Text: Jan 2023
              Type: published
              Y: 2023
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