Abstract
The current holon structures that exist so far are built to take advantage of holon dynamism through self-reconfiguration, but not in case of unexpected situations when holon behaviour is unpredicted and the dynamism is lost. In this paper, we propose a way to fill this gap by adding a model layer and a KPI layer to the holon internal structure. The specificity of these layers is that they allow both dynamic and non-dynamic reconfigurations for RMS that use holonic control. The added layer could then be used as forecasting and previewing tool and could be considered as one more step in aid in control (e.g. for digital twin), as well as an additional tool in the reconfiguration process. An application on a learning factory shows the feasibility of the proposed concept that brings perspectives on the notions of data and models aggregation.
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Acknowledgments
This research work is supported by the funding of the PhD program PERFORM (Fundamental research and development program resourcing on manufacturing) from the IRT Jules Verne (https://www.irt-jules-verne.fr/).
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Capawa Fotsoh, E., Castagna, P., Cardin, O., Kruger, K. (2021). Proposition of an Enrichment for Holon Internal Structure: Introduction of Model and KPI Layers. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_11
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