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Authors: Shailesh Tripathi ; Sonja Strasser and Herbert Jodlbauer

Affiliation: University of Applied Sciences Upper Austria, Austria

Keyword(s): Discrete Event Simulation, Network Analysis, Bipartite Graph, Community Detection, Production Planning and Control, Data Mining.

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 optimi zing 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. (More)

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Paper citation in several formats:
Tripathi, S.; Strasser, S. and Jodlbauer, H. (2021). A Network based Approach for Reducing Variant Diversity in Production Planning and Control. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA, ISBN 978-989-758-521-0; ISSN 2184-285X, pages 241-251. DOI: 10.5220/0010552402410251

@conference{data21,
author={Shailesh Tripathi. and Sonja Strasser. and Herbert Jodlbauer.},
title={A Network based Approach for Reducing Variant Diversity in Production Planning and Control},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA,},
year={2021},
pages={241-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010552402410251},
isbn={978-989-758-521-0},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA,
TI - A Network based Approach for Reducing Variant Diversity in Production Planning and Control
SN - 978-989-758-521-0
IS - 2184-285X
AU - Tripathi, S.
AU - Strasser, S.
AU - Jodlbauer, H.
PY - 2021
SP - 241
EP - 251
DO - 10.5220/0010552402410251

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