The industries' demand for appropriate solution approaches to solve complex, dynamic scheduling problems with a large amount of jobs (and operations) is steadily increasing. To meet these expectations, we developed a flexible and extendable optimization approach to deal with a variety of real-life, dynamic scheduling problems. The goal of the solution approach is to calculate feasible, good solutions for large problem instances in a short amount of time. In this paper, we describe the current

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... ate of the data model and algorithms to solve large scheduling problems with complex constraints and dynamic properties, e.g., machine capacity types, breaks on machines, setup times, etc., and give a short overview of the developed solution concept. Finally, we discuss further extensions of the given approach as well as future research directions. Abstract The industries' demand for appropriate solution approaches to solve complex, dynamic scheduling problems with a large amount of jobs (and operations) is steadily increasing. To meet these expectations, we developed a flexible and extendable optimization approach to deal with a variety of real-life, dynamic scheduling problems. The goal of the solution approach is to calculate feasible, good solutions for large problem instances in a short amount of time. In this paper, we describe the current state of the data model and algorithms to solve large scheduling problems with complex constraints and dynamic properties, e.g., machine capacity types, breaks on machines, setup times, etc., and give a short overview of the developed solution concept. Finally, we discuss further extensions of the given approach as well as future research directions.