CHAIR OF

MANAGEMENT

SCIENCE

The goal of this lecure is to provide the required background and practical skills to solve optimization problems from the area of production and logistics with computational methods. The lecture is divided into two parts:

The first part focuses on modeling and implementing deterministic planning problems from production and logistics. We will discuss:

• Basic and advanced mixed-integer programming modeling techniques
• Hands-on implementation and solution of linear programs using Gurobi and its Python API
• Application of concepts and methods for solving realistic planning problems with the help of mathematical solvers
• If required, material is provided to acquire basic Python knowledge through self-study.

The scond part focuses on implementing stochastic and dynamic problems and methodology. We will discuss:

• Creating a simulation environment for dynamic problems in Python
• Hands-on implementation of different intuitive decision strategies
• Implementation of strategies using predictions or scenarios
• Implementation of reinforcement learning methods

This lecture is self-sufficient and can be taken without prior knowledge. However, the theoretical background of the dynamic methods and more details are provided in the lecture Introduction to Dynamic Decision Problems, also offered this summer term.

The lecture will be held jointly by the Chair of Management Science and the Chair of Operations Management.