Research interests

This research area focuses on methods that exploit available data and information about future developments of customer demand and the production system itself, on the control of digitalized production systems. This is relevant, e.g., for seasonal demand or during production ramp-ups. Solution approaches that include the identification of structural properties of the underlying optimization problem in combination with simulation/optimization approaches are developed. These approaches are applied to the control of flow production lines and prioritization of repair decisions in capacitated repair shops.

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Predicting key performance measures such as throughput and cycle time is the basis for the design and control of systems that are subject to stochastic and dynamic influences (see Figure). 

This research area focuses on the development of queuing models and approaches for their quantitative analysis. Challenging is, e.g., the derivation of performance measures beyond the mean such as quantiles or service levels. This information is key for a solid risk assessment.

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The trend towards shortening product life cycles leads to more frequent changes between product generations. Managing the transition phase is challenging because of the complex interdependencies between companies’ decisions and customer reactions. In particular, substitution effects between product generations need to be captured. The research aims at deriving structural insights about the impact of finite and stochastic production capacities on strategies for the transition phase that give guidance regarding product offering, pricing, production, and inventory decisions.

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