Achieving resource- and energy-efficient system optima for production chains using cognitive self-optimization
Production systems no longer have to pursue one, but a set of goals. Classic optimization regarding lead time or capacity utilization is still sought after, but was extended by factors such as energy consumption or use of cooling lubricants. Thus the models of dependencies and system behavior become more complex, hampering optimization by classic algorithmic approaches. One subdomain of the Cluster of Excellence "Integrative Production Technology for High-Wage Countries" examines the potential of cognitive self-optimization as a way of handling technical complexity. This paper analyses the constraints and dependencies that have to be considered to find overall optima for process chains and gives an assumption of the associated complexity. This builds the base for future implementations of self-optimization to boost overall resource- and energy-efficiency in process chains. Furthermore, examples are presented on how optimization can be realized by using cognition and self-optimization.