OPM 662 (OPM 6620): Business Analytics: Capacity Optimization

Contents
The course Business Analytics: Capacity Optimization focuses on data-driven decision support for the design and management of operational systems, with a particular emphasis on capacity planning and optimization. The course introduces core concepts and tools of prescriptive analytics grounded in Operations Research.
Students learn how to formulate strategic, tactical and operational decision problems as linear programming (LP) and mixed-integer linear programming (MILP) models, with a strong focus on capacity allocation, utilization, and constraints in real-world systems. These optimization models are implemented, analyzed, and solved using standard prescriptive analytics tools.
In addition, the course introduces heuristic and approximation methods to address the complexity of large-scale capacity and scheduling problems commonly enco­untered in practice. To account for uncertainty in operational environments, data-driven and stochastic modeling approaches are also covered.
Throughout the course, students apply the methods to realistic case studies and assignments, both individually and in groups, gaining hands-on experience in modeling, solving, and interpreting capacity optimization problems that incorporate different types of variability.

Learning outcomes
Students are able to structure and analyze operational decision problems in the context of capacity optimization in operations systems. They can formulate these problems as linear and mixed-integer linear programming models and implement them using Pythonbased optimization tools (e.g., DOcplex or equivalent modeling environments) and standard solvers to derive optimal or near-optimal capacity allocation and scheduling decisions.
Students are able to apply appropriate modeling strategies to handle real-world complexity in capacity-constrained systems, including aggregation, relaxation, decomposition, and heuristic approaches. They understand how to incorporate uncertainty and variability using data-driven and stochastic modeling techniques.
In addition, students are able to interpret optimization results, perform sensitivity analyses, and trans­late model results into actionable managerial insights for capacity planning, utilization, and operational decision-making.

Necessary prerequisites
Basic knowledge in Python (OPM 560 or IS 557 or „Schlüssel­qualifikation1: Programmierkurs Python“ – Angebot der WIM) and knowledge in modelling in operations (OPM 561 or OPM 501).
In exceptional cases and by prior agreement, other modules may also be accepted as prerequisites.

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

Examiner Performing lecturer

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