Manufacturing companies face challenges when it comes to quickly adapting
their production control to fluctuating demands or changing requirements.
Control approaches that encapsulate production functions as services have shown
to be promising in order to increase the flexibility of Cyber-Physical
Production Systems. But an existing challenge of such approaches is finding a
production plan based on provided functionalities for a demanded product,
especially when there is no direct (i.e., syntactic) match between demanded and
provided functions. While there is a variety of approaches to production
planning, flexible production poses specific requirements that are not covered
by existing research. In this contribution, we first capture these requirements
for flexible production environments. Afterwards, an overview of current
Artificial Intelligence approaches that can be utilized in order to overcome
the aforementioned challenges is given. For this purpose, we focus on planning
algorithms, but also consider models of production systems that can act as
inputs to these algorithms. Approaches from both symbolic AI planning as well
as approaches based on Machine Learning are discussed and eventually compared
against the requirements. Based on this comparison, a research agenda is
derived.