In the optimization group, we examine complex optimization problems in technical and industrial settings for which no solution method is known to solve these problems optimally in an efficient time. We advise companies of all branches on simulating and solving optimization problems. For our customers, we develop concepts as well as solution methods and implement tailor-made software.
Such optimization problems are widespread and can be found in nearly all application areas and in all business lines. Well-known examples are the optimal operational planning of resources (personal, machines, money), minimization of transport costs (distance, means of transport), minimization of construction size (VLSI layout), optimization of packing densities (container packing, waste minimization), etc. Due to the complexity of these problems, commercial solutions often are not available or are still in development.
Our mission is to develop and transfer optimization methods from theoretical research to practical applications in industry and technology. We model and examine our customers' problems in the highest level of practical detail under consideration of all given constraints. We provide efficient solutions which do exactly fit our customers' needs.
In many years of experience in applying classical optimization methods to various problem settings, we have gained a multilayered knowledge regarding effectiveness and suitability of the different methods. Our optimization methods are quite different from each other, and their choice depends on the concrete problem. We employ exact and heuristic optimization methods (Branch and Bound/Cut/Price, Simulated Annealing, Great Deluge, Threshold Accepting, Record-to-Record Travel, Genetic Algorithms, Simulated Trading, Greedy, Tabu Search, Linear and Dynamic Programming, etc.).
In many cases, the step before the optimization of a production system is the identification of bottlenecks and deficits in efficiency. For this purpose, we use the means of discrete simulation and support our customers in modeling their production systems with appropriate simulation tools. From the results, we can draw conclusions about where to start the optimization.