A network based approach for reducing variant diversity in production planning and control

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

Abstract

This paper presents a network-based procedure for selecting representative materials using routings of materials as features and applies this procedure to a sheet metal processing case study which is used for parameterizing discrete event simulation models for PPC control. The discrete event simulation model (simgen) is a generic and scalable model that is commonly used to deal with optimization problems in production planning and control, such as manufacturing resource planning. The preparatory steps of discrete event simulations for production planning and control are data preprocessing, parameterization, and experimental design. Given the complexity of the manufacturing environment, discrete event simulation models must incorporate appropriate model details for parameterization and a practical approach to experimental design to ensure efficient execution of simulation models in a reasonable time. The parameterization for discrete event simulation is not trivial; it requires optimizing parameter settings for different materials dependent on routing, bill of materials complexity, and other production process-related features. For a suitable parameterization that completes the execution of discrete event simulation in an expected time, we must reduce variant diversity to an optimized level that removes redundant materials and reflects the validity of the overall production scenario. We employ a network based approach by constructing a bipartite graph and Jaccard-index measure with an overlap threshold to group similar materials using routing features and identify representative materials and manufacturing subnetworks, thus reducing the complexity of products and manufacturing routes.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021
EditorsChristoph Quix, Slimane Hammoudi, Wil van der Aalst
PublisherSciTePress
Pages241-251
Number of pages11
ISBN (Electronic)9789897585210
DOIs
Publication statusPublished - 2021
Event10th International Conference on Data Science, Technology and Applications, DATA 2021 - Virtual, Online
Duration: 6 Jul 20218 Jul 2021

Publication series

NameProceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021

Conference

Conference10th International Conference on Data Science, Technology and Applications, DATA 2021
CityVirtual, Online
Period06.07.202108.07.2021

Keywords

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

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