Improvement Heuristics for Manufacturing System Design Using Complex Network Figures

Today's manufacturing systems are characterized by a high level of structural complexity, accompanied by a diverse, less uniform material flow. As this contribution will show, this development complicates the identification of work stations that limit the logistic target achievement, because the non-trivial network effects are hard to predict. However, this identification of key work stations is particularly important during the design and operation phase of a manufacturing system, as they allow a cost-optimal system improvement with limited inference on the operation. Our approach discusses the “bottleneck” oriented approach commonly applied today in the context of network effects in manufacturing systems and compares its performance to centrality measures from complex network theory.