Prof. Dr. h.c. mult. Hasso Plattner

Data-Driven Decision-Making in Enterprise Applications

General Information

  • Teaching staff: Dr. Rainer Schlosser, Dr. Michael Perscheid
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Time: Mon 13.30 - 15.00, Thu 11.00 - 12.30
  • Room: D-E.9/10 or online (via Zoom)
  • Last Lecture: July 20, 2020

Short Description

The need for automated decision-making is steadily increasing. The goal is to derive and to implement methods for data-driven decision support for practical applications in constantly changing environments. Solving such problems requires combining data management, data science, and optimization. In general, decision problems can be described by given performance criteria, admissible decisions, constraints, and data-driven estimations of the interplay of decisions on performance. Further, every application has its own specifics, which can be exploited to solve a problem effectively. In this course, we consider different use-cases and explore suitable optimization techniques. These problems fall into the areas of (i) resource allocation management and (ii) operations management.

Goals of the Course


  • opportunities and challenges of decision-making
  • static deterministic problems
  • stochastic dynamic problems
  • optimization models and solution techniques

Do ...

  • work in small teams
  • set up suitable models, apply optimization techniques 
  • simulate controlled processes, compare performance results

Improve ...

  • mathematical, analytical, and modelling skills
  • optimization techniques
  • linear (integer) programming
  • dynamic programming
  • robust decision-making


  • the participants should be interested in mathematical methods
  • the number of participants is not restricted

Teaching and Learning Process

The course is a combination of a lecture and a practical part:

  • teachers impart relevant knowledge
  • students work on a self-containing topic in a team of ca. 3 people
  • students present their work


  • 30% Project Results (projects assigned in June)
  • 70% Project Documentation (deadline Aug 31)


Slides and Upcoming Topics

 >>> Homework #1 (to May 18): Implement Approach (iii), cf. slide 23 LPII (week 2), in AMPL based on the "project assignment" example code and study how the penalty coefficient alpha affects the allocation.

>>> Homework #2 (to May 25): Adapt the OLS AMPL model "linear regression" to a logistic regression model.

  • 5. Week: Dynamic Programming (May 25)
  • 6. Week: Market Simulation and Project Preview (June 4)
  • 7. Week: Project Assignment (June 8/11)
  • 8. Week: Project/Feedback (June 15/18)
  • 9. Week: Project/Feedback (June 22/25)
  • 10. Week: Project/Feedback (June 29/July 2)
  • 11. Week: Project/Feedback (July 6/9)

>>> Homework #3 (to July 31)

  • 12. Week: Project/Feedback (July 13/16)
  • 13. Week: Final Presentations (July 20)
  • Documentations: Deadline August 31 (ca. 15 pages)


Try AMPL: https://ampl.com/try-ampl/download-a-free-demo/