Announcement of new topics
Please note that topics are posted on a continuous basis.
Current Theses (partially in German)
Benders Decomposition for robotized delivery on the last mile
2023/05/24
Master thesis
The aim of the thesis is to understand the concept of Benders Decomposition and to apply it to the delivery of customer goods on the last mile, making use of the concept “vans and robots”. Hereby, the work of Alfandari et al. (2022) sould be understood and slightly extended.
Literature: Alfandari, L.; Ljubić, I.; da Silva, M.D.M. (2022): A tailored Benders decomposition approach for last-mile delivery with autonomous robots. European Journal of Operational Research, 299(2), 510-525.
Interested in this topic? Please use the application form on our website!
Supervisor: Prof. Dr. Felix Weidinger
The Branch-and-Bound procedure for the classical Traveling Salesman Problem: Building a Demonstrator as an Open Educational Resource
2023/05/24
Bachelor thesis
The Traveling Salesman Problem (TSP) as well as the Branch-and-Bound procedure (B&B) are both classics in Operations Research, tackled in many courses at different universities. The goal of this thesis is to develop a demonstrator which applies B&B to the TSP, using some simple bounds and branching schemes. The algorithm, hereby, needs to be visualized in a suitable manner, such that it can be used for educational purposes. The goal is to provide the demonstrator as an Open Educational Resource, ultimately, such that the outcome of the thesis can be used freely in any Operations Research course.
Interested in this topic? Please use the application form on our website!
Supervisor: Prof. Dr. Felix Weidinger
Development and benchmarking of a heuristic solution method for the picker routing problem in mixed shelves storage warehouses
2023/05/10
Bachelor thesis, Master thesis
The steady growth of the e-commerce industry, especially fuled by the pandemic, puts increased pressure on various warehouse operations. One potential approach to increase order picking efficiency is to reduce picker walking distances. The unique feature in mixed shelves storage warehouses is that items to be picked can be located in multiple storage positions in the warehouse. This results in a multi-layered optimization problem: Suitable positions must be selected as well as the shortest route between them has to be found. The goal of this thesis is to develop a heuristic solution for the picker routing problem in mixed shelves storage warehouses, to implement it and to test it against existing methods.
Interested in this topic? Please use the application form on our website!
Supervisor: Constantin Wildt, M.Sc.
Analysis of Regression Models for Optimizing Warehouse Picking Routing
2023/05/10
Bachelor thesis
This study will examine a number of regression models for optimizing warehouse picking routing. Regression models including linear regression, multiple regression, and logistic regression will be compared and analyzed. This study will also identify factors affecting regression model accuracy in a warehouse, factors such as distance, time, and item location and so on. The results of this study will provide insights into the most effective regression models for optimizing warehouse picking routing.
Interested in this topic? Please use the application form on our website!
Supervisor: Setareh Behzadi, M.Sc.
Discussion of several machine learning methods that can be used for predicting order picking times in warehouses
2023/05/10
Bachelor thesis
This topic would involve discussion about several machine learning methods such as regression analysis, decision trees, neural networks and random forests methods. The focus would be on describing property of models and identifying which data would be used to train the models in warehouse.
Interested in this topic? Please use the application form on our website!
Supervisor: Setareh Behzadi, M.Sc.
A literature review of machine/reinforcement learning methods for optimizing warehouse processes
2023/05/10
Bachelor thesis
The purpose of this literature review is to provide an overview of machine/reinforcement learning methods that can be used to optimize warehouse processes. Various types of machine/reinforcement learning algorithms will be explored in this review, including those used for inventory management, item placement, and delivery. The challenges and limitations of using machine/reinforcement learning to optimize warehouses will also be discussed in order to suggest future research directions.
Interested in this topic? Please use the application form on our website!
Supervisor: Setareh Behzadi, M.Sc.
Optimization of randomized control rules for cleaning robots
2022/03/29
Bachelor thesis
Decentralised acting cleaning robots usually operate on a static rule set. The aim of this work is to simulate different rule sets in different environments and to identify reasonable rules. Knowledge of implementation or the willingness to learn is a requirement for this.
Interested in this topic? Please use the application form on our website!
Supervisor: Prof. Dr. Felix Weidinger