Comparison of Community Detection Algorithms for Reducing Variant Diversity in Production

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

Abstract

In production planning and control, discrete-event simulation (DES) is commonly used to address optimization challenges. DES using simgen generally begins with data preprocessing, parameterization, and experiment design. However, due to the complexity of manufacturing environments, DES models require careful parameterization, with empirical experiments designed to ensure efficient execution. This parameterization involves optimizing parameter settings for different materials based on routing, bill-of-materials complexity, and other production process-related features. To achieve optimized parameterization within expected timeframes, reducing variant diversity to eliminate redundant materials is necessary by using data-driven approaches. In this study, to identify representative materials, a network-based approach with five community-detection algorithms is compared for their efficiency in execution time and efficient module detection by constructing bipartite networks of material and routing features for identifying similar material groups and representative materials. The results show that communities and subcommunities identify representative materials by significantly reducing the initial number of materials with a faster approach that can be used for DES parameterization.

Original languageEnglish
Title of host publicationAdvances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments - 43rd IFIP WG 5.7 International Conference, APMS 2024, Proceedings
EditorsMatthias Thürer, Ralph Riedel, Gregor von Cieminski, David Romero
PublisherSpringer
Pages412-427
Number of pages16
ISBN (Print)9783031716362
DOIs
Publication statusPublished - 2024
Event43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2024 - Chemnitz, Germany
Duration: 8 Sept 202412 Sept 2024

Publication series

NameIFIP Advances in Information and Communication Technology
Volume732 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2024
Country/TerritoryGermany
CityChemnitz
Period08.09.202412.09.2024

Keywords

  • Bipartite graph
  • Community detection
  • Data mining
  • Network analysis
  • Production planning and control

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