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. 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
Modules OPM 560 and OPM 561

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

Contact hoursIndependent study time
Lecture with intergrated exercise4 SWS13 SWS
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
OfferingSpring semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., M.Sc. Bus. Math.
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
Graded yes
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 & 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