Computational Logistics

What it is about

You receive a Python-based introduction to computer-aided methods for logistics problems. You model and solve classic optimization problems (e.g., transport, TSP, Vehicle Routing, lot sizing, scheduling) with Python and Gurobi, learn simulation with SimPy as well as data-driven methods and machine learning with scikit-learn, and practically implement fundamental metaheuristics. By the end you can select appropriate methods, implement them, and apply them to logistical questions as well as analyze and present results.

What you will be able to do

• Understand concepts of heuristics and metaheuristics
• Implement heuristic and metaheuristic procedures
• Numerically analyze and evaluate results
• Classify the strengths and weaknesses of different methods
• Apply the methods to a practical logistics problem in the project
• Improve scientific writing and presentation
• Collaborate in teams and allocate roles in group projects
• Prepare for the master thesis

What the module consists of

• VorlesungVermittlung der Theorie zu Heuristiken, Metaheuristiken und Matheuristiken
• Übungen (integriert)Praxisnahe Implementierung und Anwendung der Algorithmen
• GruppenprojektDurchgehendes Projekt mit Implementierung, numerischer Analyse, Bericht und Abschlusspräsentation

Teaching method

• VorlesungErläuterung der theoretischen Grundlagen
• Übung/ProgrammieraufgabenEinüben der Implementierung und Anwendung der Algorithmen
• GruppenprojektAnwendung auf ein praktisches Logistikproblem und Training wissenschaftlicher Kommunikation