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Authors: Konstantin Muehlbauer ; Lukas Rissmann and Sebastian Meissner

Affiliation: Technology Center for Production and Logistics Systems, Landshut University of Applied Sciences, Am Lurzenhof 1, Landshut, Germany

Keyword(s): Artificial Intelligence, Decision-making, Machine Learning, Order-sequence Optimization, Logistics Simulation.

Abstract: Data-oriented approaches enable new opportunities to analyze processes and support managers in decision-making during planning and control tasks. In particular, the application of simulations has been a widely used tool for many years to evaluate alternative system configurations or to predict future process outcome. Due to a rapidly changing environment in a cross-linked domain such as production and logistics systems, more and more decisions have to be made in a shorter time under consideration of multi-factorial influences. Simulation based approaches often reach limits regarding time constraints assuming limited computing power. The article describes how data, generated by production and logistics simulation can be used to train a machine learning model. Thus, the generalized framework presented can be utilized to support decision-making during planning and control tasks. By applying the framework to a case study on order sequence optimization, it was possible to verify its feasi bility and potential to improve the operational performance of a manufacturing system. (More)

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Paper citation in several formats:
Muehlbauer, K. ; Rissmann, L. and Meissner, S. (2022). Decision Support for Production Control based on Machine Learning by Simulation-generated Data. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KMIS; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 54-62. DOI: 10.5220/0011538000003335

@conference{kmis22,
author={Konstantin Muehlbauer and Lukas Rissmann and Sebastian Meissner},
title={Decision Support for Production Control based on Machine Learning by Simulation-generated Data},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KMIS},
year={2022},
pages={54-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011538000003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KMIS
TI - Decision Support for Production Control based on Machine Learning by Simulation-generated Data
SN - 978-989-758-614-9
IS - 2184-3228
AU - Muehlbauer, K.
AU - Rissmann, L.
AU - Meissner, S.
PY - 2022
SP - 54
EP - 62
DO - 10.5220/0011538000003335
PB - SciTePress

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