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Parallel ant system for the electric vehicle routing problem with time windows using CUDA.

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Title: Parallel ant system for the electric vehicle routing problem with time windows using CUDA.
Authors: Struthers, Andrew1 (AUTHOR), Davendra, Donald2 (AUTHOR) Donald.Davendra@cwu.edu
Source: Neural Computing & Applications. Apr2026, Vol. 38 Issue 7, p1-38. 38p.
Subjects: Vehicle routing problem, Ant algorithms, Genetic algorithms, Parallel programming, Heuristic algorithms, Mathematical optimization, Scheduling
Abstract: The development of electric vehicles, driven by environmental imperatives, is a rapidly growing field, particularly in the freight sector. However, widespread adoption faces unique challenges, including payload capacity, battery limitations, charging infrastructure, and charging speed. This research introduces a parallel Ant System implemented using CUDA to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW). Comprehensive experimentation was conducted on benchmark datasets, with performance compared against other heuristic approaches such as the NEH algorithm and Genetic Algorithms, leveraging a pairwise seed-based methodology. The results demonstrate significant scalability and adaptability of the proposed algorithm, achieving high-quality solutions efficiently. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature 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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DbLabel: Engineering Source
An: 192431123
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Vehicle+routing+problem%22">Vehicle routing problem</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink>
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  Data: The development of electric vehicles, driven by environmental imperatives, is a rapidly growing field, particularly in the freight sector. However, widespread adoption faces unique challenges, including payload capacity, battery limitations, charging infrastructure, and charging speed. This research introduces a parallel Ant System implemented using CUDA to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW). Comprehensive experimentation was conducted on benchmark datasets, with performance compared against other heuristic approaches such as the NEH algorithm and Genetic Algorithms, leveraging a pairwise seed-based methodology. The results demonstrate significant scalability and adaptability of the proposed algorithm, achieving high-quality solutions efficiently. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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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        Value: 10.1007/s00521-026-12041-y
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      – Code: eng
        Text: English
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        PageCount: 38
        StartPage: 1
    Subjects:
      – SubjectFull: Vehicle routing problem
        Type: general
      – SubjectFull: Ant algorithms
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Parallel programming
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Scheduling
        Type: general
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      – TitleFull: Parallel ant system for the electric vehicle routing problem with time windows using CUDA.
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            NameFull: Struthers, Andrew
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 38
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              Value: 7
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            – TitleFull: Neural Computing & Applications
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