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

Data-Driven Decision-Making in Enterprise Applications

General Information

  • Teaching staff: Dr. Matthias Uflacker, Dr. Rainer Schlosser, Martin Boissier
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Time: Mon 13.30 - 15.00, Thu 11.00 - 12.30
  • Room: D-E.9/10
  • Last lecture: July Thu 11, 2019

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% Presentations
  • 20% Project Results
  • 40% Project Documentation
  • 10% Personal Engagement


­Slides and Upcoming Topics


Homework I (to be handed in May 23, solve at least 2 problems)

A1 Master Project Assignment -> Solution A1

A2 Sudoku -> Solution A2

A3 Soccer-> Solution A3

A4 Logistic Regression -> Solution A4

Homework II (to be handed in June 13, extension for simulation June 17)

Solution Homework II


Examples & Models

Solve  LP Examples, Project AssignmentsLinear Regression using a solver.

Exercise: Code Examples (updated)

Dynamic Programming Example: Pricing Duopoly

Neos Example: Magic Square (e.g., via CPLEX




Get AMPL: https://ampl.com/products/ampl/ampl-for-students/

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

e.g. AMPL_student_windows  or  AMPL_student_macOS  (use: option solver './cplex')


PULP: http://www.optimization-online.org/DB_FILE/2011/09/3178.pdf