The term robust optimisation is particularly relevant in the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0. Robust optimisation describes methods for designing processes or decisions in such a way that they function reliably even in the event of uncertainties or unforeseen changes. The aim is to achieve good results even if, for example, data is inaccurate or conditions are unstable.
Imagine a company plans the production volume in a factory, but demand fluctuates daily. Normal optimisation could lead to bottlenecks or surpluses. Robust optimisation takes possible fluctuations into account and ensures that production remains flexible. As a result, the company is more stable and has fewer losses - even if something unforeseen happens.
Robust optimisation is particularly important when using artificial intelligence or decisions drawn from large amounts of data. It protects against failures, poor results or high costs if not everything goes as planned. Robust optimisation thus helps companies to act safely and efficiently in uncertain times.
Deutsch 
English (UK)