Application of particle swarm optimization based on clustering analysis in logistics distribution

Haobin Shi; Zhonghua Li; Wenbin Li; Zhujun Yu

doi:10.1109/ICMeCG.2009.34

Application of particle swarm optimization based on clustering analysis in logistics distribution

Haobin Shi, Zhonghua Li, Wenbin Li, Zhujun Yu

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points in need of distribution primarily by DBSCAN algorithm, and then weighted k-means algorithm is used to cluster the target points finally based on the primary clustering. Corresponding vehicles are allocated to every target cluster according to result of clustering analysis, furthermore, path of vehicles are optimized by use of PSO algorithm until all the distribution tasks are finished. Simulation experiments result shows that PSO algorithm based on clustering analysis is feasible and effective in modern logistics distribution process.

Original language	English
Title of host publication	2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009
Pages	291-295
Number of pages	5
DOIs	https://doi.org/10.1109/ICMeCG.2009.34
State	Published - 2009
Event	2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009 - Nanchang, China Duration: 16 Sep 2009 → 19 Sep 2009

Publication series

Name	2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009

Conference

Conference	2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009
Country/Territory	China
City	Nanchang
Period	16/09/09 → 19/09/09

Keywords

DBSCAN algorithm
Logistics distribution
Particle swarm optimization (PSO) algorithm
Weighted k-means algorithm

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10.1109/ICMeCG.2009.34

Cite this

Shi, H., Li, Z., Li, W., & Yu, Z. (2009). Application of particle swarm optimization based on clustering analysis in logistics distribution. In 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009 (pp. 291-295). Article 5279863 (2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009). https://doi.org/10.1109/ICMeCG.2009.34

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title = "Application of particle swarm optimization based on clustering analysis in logistics distribution",

abstract = "In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points in need of distribution primarily by DBSCAN algorithm, and then weighted k-means algorithm is used to cluster the target points finally based on the primary clustering. Corresponding vehicles are allocated to every target cluster according to result of clustering analysis, furthermore, path of vehicles are optimized by use of PSO algorithm until all the distribution tasks are finished. Simulation experiments result shows that PSO algorithm based on clustering analysis is feasible and effective in modern logistics distribution process.",

keywords = "DBSCAN algorithm, Logistics distribution, Particle swarm optimization (PSO) algorithm, Weighted k-means algorithm",

author = "Haobin Shi and Zhonghua Li and Wenbin Li and Zhujun Yu",

year = "2009",

doi = "10.1109/ICMeCG.2009.34",

language = "英语",

isbn = "9780769537788",

series = "2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009",

pages = "291--295",

booktitle = "2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009",

note = "2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009 ; Conference date: 16-09-2009 Through 19-09-2009",

}

Shi, H, Li, Z, Li, W & Yu, Z 2009, Application of particle swarm optimization based on clustering analysis in logistics distribution. in 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009., 5279863, 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009, pp. 291-295, 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009, Nanchang, China, 16/09/09. https://doi.org/10.1109/ICMeCG.2009.34

Application of particle swarm optimization based on clustering analysis in logistics distribution. / Shi, Haobin; Li, Zhonghua; Li, Wenbin et al.
2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009. 2009. p. 291-295 5279863 (2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Application of particle swarm optimization based on clustering analysis in logistics distribution

AU - Shi, Haobin

AU - Li, Zhonghua

AU - Li, Wenbin

AU - Yu, Zhujun

PY - 2009

Y1 - 2009

N2 - In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points in need of distribution primarily by DBSCAN algorithm, and then weighted k-means algorithm is used to cluster the target points finally based on the primary clustering. Corresponding vehicles are allocated to every target cluster according to result of clustering analysis, furthermore, path of vehicles are optimized by use of PSO algorithm until all the distribution tasks are finished. Simulation experiments result shows that PSO algorithm based on clustering analysis is feasible and effective in modern logistics distribution process.

AB - In order to solve the modern logistics problem of vehicle distribution, a particle swarm optimization (PSO) algorithm based on clustering analysis is proposed in this paper. This algorithm clusters the target points in need of distribution primarily by DBSCAN algorithm, and then weighted k-means algorithm is used to cluster the target points finally based on the primary clustering. Corresponding vehicles are allocated to every target cluster according to result of clustering analysis, furthermore, path of vehicles are optimized by use of PSO algorithm until all the distribution tasks are finished. Simulation experiments result shows that PSO algorithm based on clustering analysis is feasible and effective in modern logistics distribution process.

KW - DBSCAN algorithm

KW - Logistics distribution

KW - Particle swarm optimization (PSO) algorithm

KW - Weighted k-means algorithm

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BT - 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009

T2 - 2009 International Conference on Management of e-Commerce and e-Government, ICMeCG 2009

Y2 - 16 September 2009 through 19 September 2009

ER -

Application of particle swarm optimization based on clustering analysis in logistics distribution

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