@inproceedings{Phi2021,
Author = {Philipp Borchers, Dennis Lisiecki, Patrick Eschemann, Linda Feeken, Mehrnoush Hajnorouzi, Ingo Stierand},
Title = {Comparison of Production Dynamics Prediction Methods to Increase Context Awareness for Industrial Transport Systems},

Year = {2021},
Pages = {49-55},
Month = {10},
Publisher = {Reproduct NV, Ghent, Belgium},
Booktitle = {Modelling and Simulation 2021},
Organization = {EUROSIS},
type = {inproceedings},
Abstract = {The performance of factory-internal logistic systems plays a
central role for the overall productivity of the factory of the
future. A key element is the optimization of logistic systems
based on predictive analytics of transport tasks in order to
anticipate and to adapt to changes of the production flows
in the factory. Although this information might be derivable
from production plans and machine configurations, the data
is often not available for transport systems like automated
guided vehicles provided by third parties due to data protection
concerns. Here, prediction needs to be based on observable
information. In this paper, we investigate methods for the
prediction of future transport tasks between machines based on
recorded task histories. For this, we modify several prediction
methods from the literature for making them applicable to
predict timestamps and destinations of future tasks. The results
are compared with evaluation data taken from a factory
simulation via multiple metrics, which are revealing suitable
predictors based on achieved prediction accuracy.}
}
@COMMENT{Bibtex file generated on }