Computational Logistics [WI001189] (Master Program)

(Prof. Dr. Stefan Minner, Mahsa Abbaszadeh Nakhost, Alexander Jäger)
Tuesdays & Wednesdays, 08:00 - 09:30, in 0544

Course description

The course provides a Python-based introduction into computational methods and tool-boxes in logistics. It will cover analytics concepts from optimization, simulation, machine learning, and metaheuristics for analyzing logistics problems and providing state-of-the-art decision support.

After a brief introduction into the programming language Python, mathematical programming problems in logistics such as the transportation, the traveling salesman, vehicle routing and lot-sizing and scheduling problems are modeled and solved with Python-Gurobi. Advanced techniques to solve large-scale problems complement the basics.

For the simulation of logistics systems, simple queuing and inventory systems will be addressed using simpy. Advanced simulation optimization and design of experiments concepts will be illustrated using logistics problems.

For data-driven logistics methods, supervised and unsupervised machine learning algorithms using the toolbox sci-kit will be covered.

When large and complex problems cannot be solved using exact methods, metaheuristics have gained large importance. The course will cover and implement basic concepts of local search- and population-based methods.

Learning objectives

After participating in this module, students are able to understand the different concepts of optimization, simulation, machine learning, and metaheuristics for analyzing logistics problems and providing state-of-the-art decision support. They are able to implement these methods and analyze the results.

Students further comprehend the weaknesses and strengths of the methods. They are able to apply the method to a practical problem in the area of logistics in the context of a project. Through the project report and the presentations, students further improve their skills in writing academic reports and carrying out discussions within a research environment. The course will prepare the students for their master thesis.

Methods

The students will first learn to implement examples in their homeworks. They learn how to use python and the respective packages. In addition, they will work on a group project throughout the semester, which they will present in a final presentation and a report.

Grading

Homeworks and final projects (in groups of 2-3 students)

Requirements

Course “Modelling, Optimization, and Simulation” (or equivalent)