Hybridization of PSO and a discrete position update scheme techniques for manufacturing cell design

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

4 Scopus citations

Abstract

This paper proposes an hybrid algorithm for Manufacturing Cell Formation. The two techniques that are combined to address this problem correspond to Particle Swarm Optimization (PSO) and a Data Mining Clustering application. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. A maximum cell size is imposed and the number of cell is parameterizable. Some published exact results have been used as benchmarks to assess the proposed algorithm. The computational results show that the proposed algorithm is able to find the optimal solutions on almost all instances with low variability and stability.

Original languageEnglish
Title of host publicationMICAI 2008
Subtitle of host publicationAdvances in Artificial Intelligence - 7th Mexican International Conference on Artificial Intelligence, Proceedings
Pages503-512
Number of pages10
DOIs
StatePublished - 5 Dec 2008
Event7th Mexican International Conference on Artificial Intelligence, MICAI 2008 - Atizapan de Zaragoza, Mexico
Duration: 27 Oct 200831 Oct 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5317 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Mexican International Conference on Artificial Intelligence, MICAI 2008
CountryMexico
CityAtizapan de Zaragoza
Period27/10/0831/10/08

Keywords

  • Machine grouping
  • Manufacturing cells
  • Particle swarm optimization

Fingerprint Dive into the research topics of 'Hybridization of PSO and a discrete position update scheme techniques for manufacturing cell design'. Together they form a unique fingerprint.

Cite this