Our research focuses on the development of innovative solution methods for decision support in logistics, operations and supply chain management. The developed methods are applicable to strategic as well as operational optimization problems with single and multiple objectives. Current topics of interest involve, e.g., city logistics, shared mobility, disaster relief logistics, personnel scheduling, scheduling of automated guided vehicles, inventory routing and production logistics (in the context of Industry 4.0).
From a methodological point of view, our research pertains to the field of operations research, a sub-discipline of (Business) Analytics. We work on the development of
- exact optimization algorithms (Column Generation, Branch & Bound, Branch & Cut, Branch & Price)
- heuristics and metaheurstics and
- hybrid methods (combinations of exact and heuristic search techniques)
In the era of digitalization incrasing amounts of data become available which can be used as input to decision support systems. Using Predictive Analytics techniques, a number of possible future scenarios can be derived from these data and associated with different probabilites. Prescriptive Analytics concerns the application of (Operations Research) methods for decision support. In this context we use, among others, methods from the fields of stochastic, robust and simulation-based optimization.