Optimization Implementation in Production and Transportation (OPT)
| Lecturers: |
Lecture: Janis S. Neufeld & Marlin Ulmer Tutorial: Aheli Das & Jonas Stein |
| Ansprechpartner: | Aheli Das & Jonas Stein |
| Semester: | Summer term |
| Forms of instruction and credit hours: | 2 SWS lectures / 2 SWS tutorial |
| Exam form and credit points: | written exam (60 min); 5 CP |
| Lecture/ Tutorial |
Lecture: Tuesday, 1-3 PM; G22 G22A-012 Tutorial: Thursday, 1-3 PM; G22 G22A-211 Elearning |
| Language |
English |
| Inhalt der Lehrveranstaltung | The aim of the lecture is to impart the necessary background knowledge and practical skills to solve optimization problems in the field of production and logistics using computer-aided methods. The lecture is divided into two parts: The first part deals with the modeling and implementation of deterministic planning problems from production and logistics. We will discuss: - Basic and advanced modeling techniques of mixed-integer programming - Practical implementation and solution of linear programs with Gurobi and its Python API - Application of concepts and methods for solving realistic planning problems using mathematical solvers - If required, material for acquiring basic Python skills in self-study will be provided The second part will focus on the implementation of stochastic and dynamic problems and methods. We will discuss: - Creating a simulation environment for dynamic problems in Python - Practical implementation of different intuitive decision strategies - Implementation of strategies using predictions or scenarios - Implementation of reinforcement learning methods - This lecture is self-sufficient. However, the theoretical background of the dynamic methods and further details are taught in the lecture "Introduction to Dynamic Decision Problems". |