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Authors: Sonja Strasser and Andreas Peirleitner

Affiliation: University of Applied Sciences Upper Austria, Austria

ISBN: 978-989-758-255-4

Keyword(s): Clustering, Data Pre-processing, Variant Diversity, Discrete Event Simulation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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 s tudy from sheet metal processing. (More)

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Paper citation in several formats:
Strasser, S. and Peirleitner, A. (2017). Reducing Variant Diversity by Clustering - Data Pre-processing for Discrete Event Simulation Models.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 141-148. DOI: 10.5220/0006394401410148

@conference{data17,
author={Sonja Strasser. and Andreas Peirleitner.},
title={Reducing Variant Diversity by Clustering - Data Pre-processing for Discrete Event Simulation Models},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006394401410148},
isbn={978-989-758-255-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Reducing Variant Diversity by Clustering - Data Pre-processing for Discrete Event Simulation Models
SN - 978-989-758-255-4
AU - Strasser, S.
AU - Peirleitner, A.
PY - 2017
SP - 141
EP - 148
DO - 10.5220/0006394401410148

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