Christoph Kerscher, M.Sc.
Contact
Room: 1563
Tel: +49 (0)89 289 28203
christoph.kerscher(at)tum.de
Office Hours
by appointment
Research Interests
- Methods:
- Fast and scalable solution algorithms for complex combinatorial optimization problems
- Supervised and unsupervised machine learning methods
- Topics:
- Large-Scale Logistics Distribution Scenarios
- Data-Driven Optimization in Operations Management
Current Teaching (Summer 2025)
Academic Career and Positions
- Since 10/2021: Research Assistant at the Chair of Logistics and Supply Chain Management (Prof. Dr. Stefan Minner), Technical University of Munich
- 09/2024 - 12/2024: Visiting Researcher at Département Automatique Productique Informatique (Prof. Dr. Fabien Lehuédé), IMT Atlantique
Conference Presentations
- 2024: 11th INFORMS Transportation Science and Logistics Society Workshop (TSL 2024) - Nantes, France
- 2024: International Conference on Operations Research (OR24) - Munich, Germany
- 2024: 28th European Logistics Association (ELA) Doctorate Workshop - Athens, Greece
- 2023: INFORMS Transportation and Logistics Society (TSL) Second Triennial Conference - Chicago, IL, USA
Academic Services
- Since 2025: Reviewer for Transportation Research Part E: Logistics and Transportation Review
- Since 2021: Reviewer for International Journal of Production Economics
Academic Education
- 2018: Graduation as Master of Science in Industrial Engineering (M.Sc.) at University of Duisburg-Essen
- 2016: Term abroad at Nanyang Technological University, Singapore
- 2015 – 2018: Studies of Industrial Engineering at University of Duisburg-Essen
- 2015: Graduation as Bachelor of Science in Industrial Engineering (B.Sc.) at University of Magdeburg
- 2011 – 2015: Undergraduate studies of Industrial Engineering at University of Magdeburg
Professional Experience
- 2020 – 2021: Senior Analyst Strategic Fleet Management at Sixt SE, Munich
- 2018 – 2019: Consultant Data and Analytics at Ernst & Young GmbH, Munich
Master Theses:
- Meta-Controlled Hyperparameter Optimization of Heterogeneous Vehicle Routing Problem Solution Algorithms
- Using classification for model selection and design in vehicle routing
- Time-Dependent Green Vehicle Routing Problems with Heterogeneous Fleets: Modelling Approaches for Non-Linear Dynamics
- Solving vehicle routing problems with minimum utilization constraints
- Applying deep reinforcement learning in transportation optimization
- Evaluation strategies for clustering approaches in the context of vehicle routing problems
- Decomposition methods for large-scale VRPTW
- Decomposition strategies for vehicle routing problems with a heterogenous fleet
- Solving vehicle routing problems with pickup and deliveries subject to loading and unloading constraints
- Using machine learning methods to reduce large vehicle routing problems
Bachelor Theses:
- Measuring visual attractiveness in routing problems
- Real-time vehicle routing: Problem, methods, data, and evaluation
- Knowledge graphs in logistics networks: Applications and use cases for supply chain innovation
- The stochastic traveling salesman problem with temporally and spatially correlated travel times: Models, methods and applications
Project Studies:
- Clustering of vehicle routing problems with heterogeneous fleet (in cooperation SAP Labs Munich)
- Delivery route re-optimization using real-time data on mobile device (in cooperation SAP Labs Munich)
Interdisciplinary Projects for Informatics (IDP)
- Benchmark Datasets in Vehicle Routing: Summary and Implementation of the State-of-the-art (in cooperation SAP Labs Munich)
- Real-time routing strategies for the capacitated vehicle routing problem (in cooperation SAP Labs Munich)
For open topics, please see here.