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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 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 languageEnglish
Title of host publicationAdvances in Neural Network Research and Applications
Pages747-755
Number of pages9
DOIs
Publication statusPublished - 2010
Event7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China
Duration: 6 Jun 20109 Jun 2010

Publication series

NameLecture Notes in Electrical Engineering
Volume67 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference7th International Symposium on Neural Networks, ISNN 2010
Country/TerritoryChina
CityShanghai
Period6/06/109/06/10

Keywords

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

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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