Reducing Variant Diversity by Clustering: Data Pre-processing for Discrete Event Simulation Models

Sonja Straßer, Andreas Josef Peirleitner

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

Building discrete event simulation models for studying questions in production planning and control affords reasonable calculation time. Two main causes for increased calculation time are the level of model details as well as the experimental design. However, if the objective is to optimize parameters to investigate the parameter settings for materials, they have to be modelled in detail. As a consequence model details such as number of simulated materials or work stations in a production system have to be reduced. The challenge in real world applications with a high variant diversity of products is to select representative materials from the huge number of existing materials for building a simulation model on condition that the simulation results remain valid. Data mining methods, especially clustering can be used to perform this selection automatically. In this paper a procedure for data preparation and clustering of materials with different routings is shown and applied in a case study from sheet metal processing.

OriginalspracheEnglisch
TitelDATA 2017 - Proceedings of the 6th International Conference on Data Science, Technology and Applications
Redakteure/-innenJorge Bernardino, Christoph Quix, Christoph Quix, Filipe Joaquim, Filipe Joaquim
Seiten141-148
Seitenumfang8
ISBN (elektronisch)9789897582554
DOIs
PublikationsstatusVeröffentlicht - 2017
Veranstaltung6th International Conference on Data Science, Technology and Applications - Madrid, Spanien
Dauer: 24 Jun 201726 Jun 2017

Publikationsreihe

NameDATA 2017 - Proceedings of the 6th International Conference on Data Science, Technology and Applications

Konferenz

Konferenz6th International Conference on Data Science, Technology and Applications
Land/GebietSpanien
OrtMadrid
Zeitraum24.06.201726.06.2017

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