Henning Blunck

• The distribution of control capabilities and functions among autonomous system components has attracted extensive research in the fields of logistics and production planning & control (PPC). Their emergent nature, however, renders much of the traditional, reductionist knowledge about the design of manufacturing systems and their control void, opening a gap in the understanding that is already threatening the industrial adoption of distributed PPC approaches. The current thesis addresses this particular research gap. It is driven in especially by the frequently expressed hypothesis that a combination of classical, centralized production control and new, distributed forms can yield optimal performance. This hypothesis is explored through a combination of interdisciplinary literature review and minimal model investigations. Cellular Automata on networks of different structure are applied to investigate the role of control network hierarchy on the performance of agents in simple, distributed problem solving settings, finding not only a performance peak at “medium” levels of hierarchy, but also developing a mechanistic understanding for it. The second quantitative model borrows from findings in algorithmic game theory to explore how the emergent behavior of selfish agents can be reconciled with the established ideal in manufacturing system design to set target utilization levels for machines. The findings of this thesis support a design approach for distributed Production Planning & Control (PPC) systems based on evidence and analysis, instead of experience and experimentation. It enhances our understanding of the success factors of distributed control in production environments and beyond. It can advance the development of “emergence engineering” by providing a deeper understanding of the target-driven design of Complex Adaptive System (CAS).