The Chair's main field of research encompasses various managerial decisions in production and operations management. Current research projects particularly focus on quantitative decision support in the design and management of production systems. The corresponding application areas are wide and include, among others, assemble to order (ATO) production systems, flow lines in the automotive industry, airport operations, or call center operations.
In this field, our focus is on scheduling operations in a multi-product setting, i.e., allocating heterogeneous jobs to (heterogeneous) resources. We consider both tactical and operational planning situations with sequence-dependent processing times and time-window constraints. Additionally, different aspects of fairness are considered, especially in workforce planning and task assignment.
Management of dynamic systems
Another stream of our research is related to tactical and strategic planning of capacity under random and time-dependent demand and service level restrictions. In this setting, production processes are often highly time-dependent, e.g., due to the influence of capacity ramp-ups, seasonal demand patterns, or time-dependent reliability. We support diverse managerial decisions in such stochastic and time-dependent environments.
Design of lean operations systems
This research area is related to the analysis and optimization of the performance of manufacturing systems under stochastic conditions, e.g., due to uncertain supplier's capacities, machine breakdowns, or customer demand variability. The aim is to support decisions in the strategic configuration of lean production systems that are robust with respect to planned and unforeseen changes.
As shown in the figure above, several operations research techniques are used to analyze and to optimize production and operations systems under static and dynamic conditions.
Methods from discrete optimization are applied to deterministic planning tasks. Quantitative models of decision problems are derived and solved using optimization algorithms. These include, e.g., Benders decomposition, Branch & Bound, and dynamic programming. For large-scale optimization problems, efficient heuristic solution approaches are developed.
For the performance analysis of queueing systems, standard methods of queueing theory are applied to analyze stationary models. Growing emphasis has been placed on the analysis of random and time-dependent queueing systems. Besides simulation studies, fast and reliable approximation approaches are developed to support managerial decisions in such stochastic and dynamic environments.
The third stream combines both former operations research directions into the robust optimization of stochastic systems. Here, the capacity of servers, the size of the system, or the acceptance or release of orders are considered as decision variables in an uncertain environment. To solve such Stochastic Programming problems, advanced decomposition and sampling approaches are developed and analyzed to support robust managerial decisions.