  Industrial manufacturing is currently amidst it's fourth great revolution,
pushing towards the digital transformation of production processes. One key
element of this transformation is the formalization and digitization of
processes, creating an increased potential to monitor, understand and optimize
existing processes. However, one major obstacle in this process is the
increased diversification and specialisation, resulting in the dependency on
multiple experts, which are rarely amalgamated in small to medium sized
companies. To mitigate this issue, this paper presents a novel approach for
multi-criteria optimization of workflow-based assembly tasks in manufacturing
by combining a workflow modeling framework and the HeuristicLab optimization
framework. For this endeavour, a new generic problem definition is implemented
in HeuristicLab, enabling the optimization of arbitrary workflows represented
with the modeling framework. The resulting Pareto front of the multi-criteria
optimization provides the decision makers a set of optimal workflows from which
they can choose to optimally fit the current demands. The advantages of the
herein presented approach are highlighted with a real world use case from an
ongoing research project.
