Current Research

At present, I am engaged in various projects that are briefly described below. Please feel free to contact me for additional information or access to working papers. My current research predominantly relies on operational research techniques, such as Linear Programming and Reinforcement Learning, which I apply to real-world settings following the idea of field experiments. In addition, I am also involved in publishing tutorials, teaching reports, and results from master theses.

Emission reduction strategies in operational transportation

The objective of this project is to extend my previous research on eco-labels and emission limits in transportation by comparing the efficacy of different emission reduction strategies, including eco-labels, emission targets, and carbon taxes, in real-world transportation scenarios. This research is crucial as it will provide valuable insights into which policy measures can effectively reduce emissions from the transportation sector, which is one of the largest contributors to greenhouse gas emissions globally.

Optimal loading and unloading strategies of RoRo-ships using 5G data. 

This research project aims to model the RoRo ship loading and unloading optimization problem as a sequential decision process, taking into account various levels of data availability achievable with 5G technology. Subsequently, we will utilize custom policies from approximate dynamic programming to identify optimal solutions, which will be implemented at the Port of Kiel. This research is part of the BMBV-funded Förde 5G project, and we are also in the process of establishing a research collaboration with the Technical University of Denmark (DTU). 

Network design of biogas plants in Northern Germany

Biogas production with on-site electricity generation plays a significant role in Germany's renewable energy landscape. However, the current business model heavily relies on government support, primarily through feed-in and premium tariffs, which are gradually set to expire. In light of this, this project aims to explore an alternative business model for biogas plants. The proposed model involves connecting decentralized plants through a pipeline network to a central processing terminal, which offers enhanced upgrading efficiency and cost-effectiveness. To determine the optimal solution, a mixed-integer linear programming model is employed to analyze and optimize the pipeline network for self-contained subnetworks. The findings from this model serve as a basis for evaluating the feasibility and effectiveness of the proposed alternative business model. This project is conducted in collaboration with the Institute of Agricultural and Nutritional Sciences at Kiel University.