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Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation.

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Titel: Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation.
Autoren: Liu, Zhengyuan, Wang, Qinghua
Quelle: IET Communications (Wiley-Blackwell); Oct2022, Vol. 16 Issue 16, p1957-1972, 16p
Schlagwörter: ANT algorithms, DRONE aircraft delivery, ALGORITHMS, IMPACT loads, TASKS
Abstract: This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]
Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell 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.)
Datenbank: Complementary Index
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  Label: Title
  Group: Ti
  Data: Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhengyuan%22">Liu, Zhengyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qinghua%22">Wang, Qinghua</searchLink>
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  Data: IET Communications (Wiley-Blackwell); Oct2022, Vol. 16 Issue 16, p1957-1972, 16p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22ANT+algorithms%22">ANT algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22DRONE+aircraft+delivery%22">DRONE aircraft delivery</searchLink><br /><searchLink fieldCode="DE" term="%22ALGORITHMS%22">ALGORITHMS</searchLink><br /><searchLink fieldCode="DE" term="%22IMPACT+loads%22">IMPACT loads</searchLink><br /><searchLink fieldCode="DE" term="%22TASKS%22">TASKS</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell 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:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1049/cmu2.12451
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1957
    Subjects:
      – SubjectFull: ANT algorithms
        Type: general
      – SubjectFull: DRONE aircraft delivery
        Type: general
      – SubjectFull: ALGORITHMS
        Type: general
      – SubjectFull: IMPACT loads
        Type: general
      – SubjectFull: TASKS
        Type: general
    Titles:
      – TitleFull: Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation.
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      – PersonEntity:
          Name:
            NameFull: Liu, Zhengyuan
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          Name:
            NameFull: Wang, Qinghua
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          Dates:
            – D: 01
              M: 10
              Text: Oct2022
              Type: published
              Y: 2022
          Identifiers:
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              Value: 17518628
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            – Type: volume
              Value: 16
            – Type: issue
              Value: 16
          Titles:
            – TitleFull: IET Communications (Wiley-Blackwell)
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