OPM 662: Business Analytics: Modeling and Optimization

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).

Forms of teaching and learningContact hoursIndependent study time
Lecture with integrated exercise4 SWS9 SWS
Exercise class2 SWS8 SWS
ECTS credits8
Graded yes
Form of assessmentAssignments and presentations (70%), written exam (45 min) or oral exam (30%)
Restricted admissionyes
Further information
Performing lecturer
Prof. Dr. Raik Stolletz
Prof. Dr. Raik Stolletz
Frequency of offeringSpring semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., M.Sc. Wirt. Math.
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
LiteratureWill be announced during the lecture
Course outlinePlease 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
  • Heuristic solutions for large-scale problems
  • Stochastic Optimization
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