Adaptation and Learning in Distributed Production Control

Distributed (agent-based) control architectures offer prospects of reduced complexity, high flexibility and a high robustness against disturbances in manufacturing. However, it has also turned out that distributed control architectures, usually banning all forms of hierarchy, cannot guarantee optimum performance and the system behaviour can be unpredictable. In the paper machine learning approaches such as neurodynamic programming and simulated annealing are described for managing changes and disturbances in manufacturing systems, and to decrease the computational costs of the scheduling process. The results demonstrate the applicability of the proposed solutions, which can contribute to significant improvements in system performance, keeping the known benefits of distributed control.