A PSO-based clustering algorithm for manufacturing cell design

Orlando Durán, Nibaldo Rodriguez, Luiz Airton Consalter

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

23 Scopus citations

Abstract

Since the last years different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing Cell Formation using a modified particle swarm optimisation (PSO) algorithm. The main modification made to the original PSO algorithm consists on that in this work it is not used the vector of velocities as the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining techniques. Some simulations are presented and compared. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. The computational results show that the PSO algorithm is able to find the optima! solutions on almost all instances.

Original languageEnglish
Title of host publicationProceedings - 1st International Workshop on Knowledge Discovery and Data Mining, WKDD
Pages72-75
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event1st International Workshop on Knowledge Discovery and Data Mining, WKDD - Adelaide, Australia
Duration: 23 Jan 200824 Jan 2008

Publication series

NameProceedings - 1st International Workshop on Knowledge Discovery and Data Mining, WKDD

Conference

Conference1st International Workshop on Knowledge Discovery and Data Mining, WKDD
Country/TerritoryAustralia
CityAdelaide
Period23/01/0824/01/08

Keywords

  • Machine grouping
  • Manufacturing cells
  • Particle swarm optimization

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