Improved clustering algorithm for design structure matrix

Fredrik Borjesson, Katja Hölttä-Otto

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

33 Citations (Scopus)

Abstract

For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic, and to be certain a very good clustering solution has been obtained, it may be necessary to run the algorithm thousands of times. To make this feasible in practice, the algorithm must be computationally efficient. Two algorithmic improvements are presented. Together they improve the quality of the results obtained and increase speed significantly for normal clustering problems. The proposed new algorithm is applied to a cordless handheld vacuum cleaner.

Original languageEnglish
Title of host publicationASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012
Pages921-930
Number of pages10
Volume3
EditionPARTS A AND B
DOIs
Publication statusPublished - 2012
MoE publication typeA4 Article in a conference publication
EventASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference - St Louis, United States
Duration: 16 Aug 202019 Aug 2020

Conference

ConferenceASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Abbreviated titleIDETC/CIE
CountryUnited States
CitySt Louis
Period16/08/202019/08/2020

Keywords

  • Clustering algorithm
  • Design structure matrix
  • Stochastic hill-climbing

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