Scalable co-clustering using a crossing minimization - Application to production flow analysis

Csaba Pigler, Ágnes Fogarassy-Vathy, J. Abonyi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Production flow analysis includes various families of components and groups of machines. Machine-part cell formation means the optimal design of manufacturing cells consisting of similar machines producing similar products from a similar set of components. Most of the algorithms reorders of the machine-part incidence matrix. We generalize this classical concept to handle more than two elements of the production process (e.g. machine - part - product - resource - operator). The application of this extended concept requires an efficient optimization algorithm for the simultaneous grouping these elements. For this purpose, we propose a novel co-clustering technique based on crossing minimization of layered bipartite graphs. The present method has been implemented as a MATLAB toolbox. The efficiency of the proposed approach and developed tools is demonstrated by realistic case studies. The log-linear scalability of the algorithm is proven theoretically and experimentally.

Original languageEnglish
Pages (from-to)209-228
Number of pages20
JournalActa Polytechnica Hungarica
Volume13
Issue number2
Publication statusPublished - 2016

Keywords

  • Cell formation
  • Co-clustering
  • Co-crossing minimization

ASJC Scopus subject areas

  • General
  • Engineering(all)

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