Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system

Bo Xing; Wen Jing Gao; Fulufhelo V. Nelwamondo; Kimberly Battle; Tshilidzi Marwala

doi:10.1007/978-3-642-12990-2_87

Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system

Bo Xing
, Wen Jing Gao
, Fulufhelo V. Nelwamondo
, Kimberly Battle
, Tshilidzi Marwala

Faculty of Engineering and the Built Environment

University of Johannesburg
Council for Scientific and Industrial Research

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

12 Citations (Scopus)

Abstract

The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.

Original language	English
Title of host publication	Advances in Neural Network Research and Applications
Pages	747-755
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-12990-2_87
Publication status	Published - 2010
Event	7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China Duration: 6 Jun 2010 → 9 Jun 2010

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	67 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	7th International Symposium on Neural Networks, ISNN 2010
Country/Territory	China
City	Shanghai
Period	6/06/10 → 9/06/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Adaptive resonance theory neural network
Ant colony system
Group technology
Grouping efficiency
Part-machine clustering

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-3-642-12990-2_87

Cite this

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title = "Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system",

abstract = "The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.",

keywords = "Adaptive resonance theory neural network, Ant colony system, Group technology, Grouping efficiency, Part-machine clustering",

author = "Bo Xing and Gao, \{Wen Jing\} and Nelwamondo, \{Fulufhelo V.\} and Kimberly Battle and Tshilidzi Marwala",

year = "2010",

doi = "10.1007/978-3-642-12990-2\_87",

language = "English",

isbn = "9783642129896",

series = "Lecture Notes in Electrical Engineering",

pages = "747--755",

booktitle = "Advances in Neural Network Research and Applications",

note = "7th International Symposium on Neural Networks, ISNN 2010 ; Conference date: 06-06-2010 Through 09-06-2010",

}

Xing, B, Gao, WJ, Nelwamondo, FV, Battle, K & Marwala, T 2010, Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system. in Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol. 67 LNEE, pp. 747-755, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, 6/06/10. https://doi.org/10.1007/978-3-642-12990-2_87

Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system. / Xing, Bo; Gao, Wen Jing; Nelwamondo, Fulufhelo V. et al.
Advances in Neural Network Research and Applications. 2010. p. 747-755 (Lecture Notes in Electrical Engineering; Vol. 67 LNEE).

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

TY - GEN

T1 - Part-machine clustering

T2 - 7th International Symposium on Neural Networks, ISNN 2010

AU - Xing, Bo

AU - Gao, Wen Jing

AU - Nelwamondo, Fulufhelo V.

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AU - Marwala, Tshilidzi

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N2 - The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.

AB - The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.

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KW - Ant colony system

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Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this