Master Theses

As part of your master thesis, you will work independently on a topic in the field of Management Science.

  • The Chairs of Management Science and Operations Management organize access to Master theses topics together.

Hence, in order to apply for a Master’s thesis topic, it is mandatory to hand in the following form (at our office at the Chair of Management Science): Application form Master’s thesis & Colloquium. In the application, please indicate whether you are interested to be perform a topic on Management Science or Operations Management or leave this open. Please consider, that writing a master thesis at the Chair of Operations Management cannot be guaranteed as the new chairholder is not known, yet.

Please hand the application in together with a current transcript of records and a letter of motivation (in the language you intend to write your thesis in). You can submit the documents via email to

The deadlines for submissions are near the end of the ongoing semester:

  • Summer term 2020 (for Master thesis begin in the next winter term): 1st July 2020

Note that we expect you to have taken a Scientific Seminar as well as a Scientific Project at one of our chairs.

After the application has been accepted, we negotiate a topic with you. Topics can come from two sources:

  • We propose topics before the application deadline. (Please indicate if you are interested in one of these in your application).
  • You propose a topic within an exposé of 1 to 2 pages which is related to our research and the master classes offered by our group.

Please understand that we reserve the right to reject topics. Please do not hesitate to contact us if you have any further questions on the topics we suggested.

Potential master thesis topics for the winter term 2020/21:

The following list gives you an overview of potential topics. We will negotiate the particular topic with you once you actually begin with your master thesis.

Dr. Janis Sebastian Neufeld:

  1. Integrated Vehicle Routing and Crew Scheduling
    • Lam, E., Van Hentenryck, P., & Kilby, P. (2020). Joint Vehicle and Crew Routing and Scheduling. Transportation Science (2), 488-511.
  2. Heuristics for Capacitated Lot-Sizing Problems with Parallel Machines
    • Benjamin Vincent, Christophe Duhamel, Libo Ren & Nikolay Tchernev (2020): A population-based metaheuristic for the capacitated lot-sizing problem with unrelated parallel machines, International Journal of Production Research.
  3. Machine Learning Approaches for Scheduling Problems
    • Benda, F., Braune, R., Doerner, K. F., & Hartl, R. F. (2019). A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking. OR Spectrum, 41(4), 871-893.
  4. Reinforcement Learning Enhanced Meta-Heuristics for Flowshop Scheduling
    • Reyna, Y. C. F., Cáceres, A. P., Jiménez, Y. M., & Reyes, Y. T. (2019). An Improvement of Reinforcement Learning Approach for Permutation of Flow-Shop Scheduling Problems. Revista Ibérica de Sistemas e Tecnologias de Informação, (E18), 257-270.
  5. Energy-Aware Scheduling with Energy-Price Forecasts
    • Windler, T., Busse, J., & Rieck, J. (2019). One month-ahead electricity price forecasting in the context of production planning. Journal of Cleaner Production, 238.
  6. Location routing problems for integrated mail, newspaper and parcel delivery
    • Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308.

 Dr. Tino Henke:

  1. A Review of Innovative Urban Delivery Approaches
    • Savelsbergh, M., & van Woensel, T. (2016). City Logistics: Challenges and Opportunities. In: Transportation Science 50, 579-590.
  2. Urban Deliveries with Cargo Bikes: Concepts from Practice and an Overview of Optimization Problems
    • Anderluh, A., Hemmelmayr, V.C., & Nolz, P.C. (2017). Synchronizing Vans and Cargo Bikes in a City Distribution Network. In: Central European Journal of Operations Research 25, 345-376.
  3. Urban Deliveries via Public Transport Systems: Concepts from Practice and an Overview of Optimization Problems
    • Masson, R., Trentini, A., Lehuédé, F., Malhéné, N., Péton, O., & Tlahig, A. (2017). Optimization of a city logistics transportation system with mixed passengers and goods. In: EURO Journal on Transportation and Logistics 6, 81-109.
  4. Collaboration Approaches in Urban Delivery: Concepts from Practice and an Overview of Optimization Problems
    • Cleophas, C., Cottrill, C., Ehmke, J.F., & Tierney, K. (2019). Collaborative urban transportation: Recent advances in theory and practice. In: European Journal of Operational Research 273, 801-816.
  5. Determining Proctor Schedules for University Exams
    • Awad, R.M., & Chinneck, J.W. (1998). Proctor Assignment at Carleton University. In: Interfaces 28, 58–71.

 Thomas Horstmannshoff:

  1. Using Blockchain in Personal Mobility Applications
    • Saranti P. G., Chondrogianni D., & Karatzas S. (2019). Autonomous Vehicles and Blockchain Technology Are Shaping the Future of Transportation. In: Nathanail E., Karakikes I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham.
  2. Applying Multi-Criteria Optimization in Multimodal Route Planning
    • Bast H. et al. (2016). Route Planning in Transportation Networks. In: Kliemann L., Sanders P. (eds) Algorithm Engineering. Lecture Notes in Computer Science, vol 9220. Springer, Cham.
  3. Recent Advances in Interactive Multi-Criteria Optimization
    • Miettinen, Kaisa (2013). Nonlinear Multiobjective Optimization: Springer.
  4. Customer Empowerment in the Context of Personal Mobility Planning
    • Alt et al. (2019). Towards customer-induced service orchestration - requirements for the next step of customer orientation. In: Electronic Markets, 29(1), 7991.

Jarmo Haferkamp:

  1. Implementation of an Iterative Local Search to Solve the Team Orienteering Problem of a Ride-Sharing Service
    • Vansteenwegen, P., Souffriau, W., Vanden Berghe, G., & van Oudheusden, D. (2009). Iterated local search for the team orienteering problem with time windows. Computers & Operations Research, 36(12), 3281–3290.
  2. Adapting Customer Acceptance Mechanisms for On-Demand Ride-Sharing Services
    • Köhler, C., Ehmke, J. F., & Campbell, A. M. (2020). Flexible time window management for attended home deliveries. Omega, 91, 102023.
  3. Recent Advances Towards the Dynamic Dial-a-Ride Problem
    • Cordeau, J.-F., & Laporte, G. (2007). The dial-a-ride problem: models and algorithms. Annals of Operations Research, 153(1), 29–46.
  4. Approaches for Solving the Same-Day Delivery Problem
    • Voccia, S. A., Campbell, A. M., & Thomas, B. W. (2019). The Same-Day Delivery Problem for Online Purchases. Transportation Science, 53(1), 167–184.

Rico Kötschau:

  1. Impact of Autonomous Vehicles on the Vehicle Routing Problem
    • Vareias, A. D., Repoussis, P. P., & Tarantilis, C. D. (2019). Assessing customer service reliability in route planning with self-imposed time windows and stochastic travel times. Transportation Science, 53(1), 256-281.
  2. Approaches to Guarantee Punctual Arrival Times for Shuttle Services
    • Gurumurthy, K. M., Kockelman, K. M., & Loeb, B. J. (2019). Sharing vehicles and sharing rides in real-time: Opportunities for self-driving fleets. In Advances in Transport Policy and Planning (Vol. 4, pp. 59-85). Academic Press.
  3. Vehicle Routing Problem with Heterogeneous Fleets: Concepts from Practice and an Overview of Optimization Problems
    • Hoff, A., Andersson, H., Christiansen, M., Hasle, G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Fleet composition and routing. In: Computers & Operations Research, 37(12), 2041-2061.
  4. Strategic Fleet Composition: Concepts from Practice and an Overview of Optimization Approaches
    • Pinto, R., Lagorio, A., & Golini, R. (2018). Urban Freight Fleet Composition Problem. In: IFAC-PapersOnLine, 51(11), 582-587.
  5. Using Reinforcement Learning Approaches to Solve the Vehicle Routing Problem
    • Mao, C., & Shen, Z. (2018). A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network. In: Transportation Research Part C: Emerging Technologies, 93, 179-197.


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