Modul

The assessment is carried out as partial exams of the single courses of this module, whose sum of credits must meet the minimum requirement of credits of this module. The assessment procedures are described for each course of the module seperately. The overall grade of the module is the average of the grades for each course weighted by the credits and truncated after the first decimal.

The student

• knows and describes the basic concepts of advanced optimization methods, especially from dynamic optimization in macroeconomics, mixed-integer, global, high-dimensional, nature-inspired and stochastic optimization as well as from location planning and supply chain management
• Knows the methods and models essential for quantitative analysis,
• models and classifies optimization problems and selects appropriate solution procedures to solve even challenging optimization problems independently and, if necessary, with computer assistance,
• validates, illustrates and interprets obtained solutions,
• recognizes disadvantages of solution methods and, if necessary, is able to make suggestions for their adaptation to practical problems.

Please check the individual courses for any prerequisites and recommendations

The module focuses on teaching both theoretical foundations and solution methods for optimization problems relevant in the field of Digital Economics.

The total workload for this module is approximately 270 hours. The exact distribution is made according to the credit points of the courses of the module.