Optimization Implementation in Production and Transportation (OPT)
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Lecturers
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Lecture: Janis S. Neufeld & Marlin Ulmer
Tutorial: Aheli Das & Jonas Stein
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Contact person
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Semester
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summer term
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Forms of instruction and credit hours
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2 SWS lectures / 2 SWS tutorial
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Exam form and credit points
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written exam (60 min); 5 CP
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Lecture/ Tutorial
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Language
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English
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Objectives and Contents
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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 providedThe 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".
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