OPM 662: Business Analytics: Modeling and Optimization

Contents
Business Analytics helps to optimize decisions for the design and management of operations systems and production processes. This course introduces concepts and tools for prescriptive analytics for modeling and optimization based on techniques from Operations Research. Operational and tactical planning tasks are formulated as linear and mixed integer linear programming models. Optimization models are analyzed and implemented in class. They are solved using standard tools of prescriptive analytics. Different heuristic techniques to cope with the complexity of real-world scheduling problems are introduced and implemented. Data-driven approaches to cope with stochastic variability are introduced and analyzed. During the course the students will work on several case studies and assignments (individual and in groups).

Learning outcomes
Students learn how to structure operations planning and scheduling problems. They are able to trans­late them into mixed integer linear models. Students learn how to use Python to implement them and solve them with a standard solver to derive optimal plans/schedules (DOcplex Python Modeling API). They also learn to deal with the complexity of real-world problems (e.g., via aggregation, relaxation, and decomposition techniques) and how to perform sensitivity analyses in order to obtain useful managerial insights.

Necessary prerequisites
OPM 561 and OPM 560 OR: OPM 561 and „Schlüssel­qualifikation 1: Programmierkurs Python“ (Angebot der WIM)

Recommended prerequisites
The course assumes a basic knowledge in mathematics (including linear programming).

Examiner Performing lecturer

Prof. Dr. Raik Stolletz Prof. Dr. Raik Stolletz