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Complex project scheduling problems arise in many different settings and the quality of a schedule typically has a tremendous impact on the efficiency, timeliness, and cost of a project. However, creating good schedules manually is expensive and error-prone, which is why a lot of research has been done on automating this process. Due to the multitude of different problem settings, each with their own unique set of requirements and features which require specialized solution approaches, manual scheduling is still widespread in many areas. One such area is that of industrial test laboratories, where products need to be tested and certified according to a wide range of specifications and international norms.

In this thesis, we introduce a new and complex project scheduling problem that is designed to model the requirements of industrial test laboratories. It features heterogeneous resources with different capabilities as well as a unique grouping aspect, which serves to improve the flexibility of the schedules and reduce unnecessary overheads. We provide a formal definition of this problem, as well as a set of benchmark instances of varying sizes, both real-world instances taken directly from the laboratory of our industrial partner and randomly generated ones using a new and configurable instance generator.