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Citation link: https://doi.org/10.26092/elib/500
Dissertation_Funke.pdf
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Analyzing and Predicting Material Flow Networks Using Stochastic Block Models and Statistical Graph Embeddings


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Authors: Funke, Thorben  
Supervisor: Freitag, Michael  
1. Expert: Freitag, Michael  
2. Expert: Becker, Till  
Abstract: 
Manufacturing and logistics systems consist of many complexly interacting elements. Starting from social science, the field of complex networks has developed concepts and methods to analyze and predict networks, such as friendship networks or protein interactions. However, although these examples have equivalents in the form of company networks and interactions within manufacturing processes, more sophisticated methods have not yet been transferred to manufacturing and logistics. We propose to apply methods from clustering and graph embedding on representations of machine interactions to analyze the structural stability of manufacturing systems and to predict structural changes of such systems.
Keywords: complex networks; graph clustering; graph representations; material flow networks
Issue Date: 10-Dec-2020
Type: Dissertation
DOI: 10.26092/elib/500
URN: urn:nbn:de:gbv:46-elib47039
Institution: Universität Bremen 
Faculty: Fachbereich 04: Produktionstechnik, Maschinenbau & Verfahrenstechnik (FB 04) 
Appears in Collections:Dissertationen

  

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