Hasso-Plattner-Institut
Prof. Dr. h.c. Hasso Plattner
  
 

Data-Driven Decision-Making in Enterprise Applications

General Information

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

Understand...

  • 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

Preconditions

  • the participants should be interested in mathematical methods
  • the number of participants is restricted to 25

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

Grading

  • 30% Presentations
  • 20% Project Results
  • 40% Project Documentation
  • 10% Personal Engagement

Material/Preparation

Slides and Upcoming Topics

  • 1st Meeting: Introduction (Thu April 18)

Exercises

Related Work