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. All lectures will be given in a computer lab, where the optimization models are implemented and 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).

Forms of teaching and learning	Contact hours	Independent study time
Lecture with integrated exercise	4 SWS	9 SWS
Exercise class	2 SWS	8 SWS

ECTS credits	8
Graded	yes
Workload	240h
Language	English
Form of assessment	Assignments and presentations (70%), written exam (45 min) or oral exam (30%)
Restricted admission	yes
Further information	–

Examiner

Performing lecturer

Prof. Dr. Raik Stolletz

Frequency of offering	Spring semester
Duration of module	1 semester
Range of application	M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., M.Sc. Bus. Math.
Preliminary course work	–
Program-specific Competency Goals	CG 1, CG 4
Literature	Will be announced during the lecture
Course outline	Please note that this outline will be subject to changes: Applications of optimization models Aggregated production planning Lot sizing and detailed scheduling Workforce planning Business Analytics approaches Mathematical modeling & optimization algorithms Heuristic solutions for large-scale problems Scenario approaches for robust planning Managerial insights and numerical studies Design of numerical studies Sensitivity analysis Interpretation of solutions Practical insights Business Analytics tool for modeling and optimization Guest lecture by business analytics professionals