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 the office of Operations Management): Application form Master’s thesis & Colloquium. In the application, please indicate whether you are interested to be perform a topic on Operations Management or Management Science or leave this open.

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 in one of the following ways:

  • Send them via E-mail to bwl-pl@ovgu.de
  • Leave them at the office of the Chair of Operations Management (G22-E004)
  • Put them in the mailbox of the Chair of Operations Management (G22-B, first floor)
  • Send them via mail to: Otto-von-Guericke-Universität Magdeburg, Fakultät für Wirtschaftswissenschaft, Lehrstuhl für Operations Management, Postfach 4120, 39016 Magdeburg.

The deadlines for submissions are in the last week of lectures of the ongoing semester:

  • Winter term 2019/20 (for Master thesis begin in the next summer term): 1st February 2020

Note that we expect you to have taken our Scientific Seminar as well as our Scientific Project.

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 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 summer term 2020:

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.

  1. Heuristic Approaches for Home Health Care Routing and Scheduling
    • Grenouilleau, F., Legrain, A., Lahrichi, N., & Rousseau, L. M. (2019). A set partitioning heuristic for the home health care routing and scheduling problem. European Journal of Operational Research, 275(1), 295-303.
  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. Solving Large-Scale Problem by Distributed Algorithms
    • Sun, L., Lin, L., Li, H., & Gen, M. (2019). Large scale flexible scheduling optimization by a distributed evolutionary algorithm. Computers & Industrial Engineering, 128, 894-904.
  4. 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.
  5. 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.

 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. Using Drones and Robots for Urban Delivery: Concepts from Practice and an Overview of Optimization Problems
    • Dorling, K., Heinrichs, J., Messier, G.G., & Magierowski, S. (2017). Vehicle Routing Problems for Drone Delivery. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 70-85.
  3. Crowdshipping in Urban Delivery: Concepts from Practice and an Overview of Optimization Problems
    • Archetti, C., Savelsbergh, M., & Speranza, M.G. (2016). The Vehicle Routing Problem with Occasional Drivers. In: European Journal of Operational Research 254, 472-480.
  4. Trunk Deliveries: Concepts from Practice and an Overview of Optimization Problems
    • Reyes, D., Savelsbergh, M., & Toriello, A. (2017). Vehicle Routing with Roaming Delivery Locations. In: Transportation Research Part C: Emerging Technologies 80, 71-91.
  5. 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.
  6. 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. Using Reinforcement Learning Approaches in Personal Mobility Planning
    • Vermorel J., & Mohri M. (2005). Multi-armed Bandit Algorithms and Empirical Evaluation. In: Gama J., Camacho R., Brazdil P. B., Jorge A. M., Torgo L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science, vol 3720. Springer, Berlin, Heidelberg.
  3. 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.
  4. Recent Advances in the Multimodal Team Orienteering Problem
    • Gunawan A. et al. (2016). Orienteering Problem: A survey of recent variants, solution approaches and applications. In: European Journal of Operation Research, 255(2), 315-332.

Jarmo Haferkamp:

  1. Dispatching Strategies for Autonomous Vehicles
    • Hyland, M., & Mahmassani, H. S. (2018). Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests. In: Transportation Research Part C: Emerging Technologies, 92, 278-29
  2. Recent Advances in Algorithms for Large-scale Dynamic Ridesharing Services
    • Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D. (2017). On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. U.S.A..
  3. Effects of Autonomous Vehicles on Urban Traffic: An Analysis of Simulation Studies
    • Bischoff, J., Maciejewski, M., & Nagel, K. (2017). City-wide shared taxis: A simulation study in Berlin. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC),  275-280.
  4. Delivery Cost Approximation for Attended Home Deliveries
    • Köhler, C., & Haferkamp, J. (2018). Evaluation of Delivery Cost Approximation for Attended Home Deliveries. In: Transportation Research Procedia, 2018.

Rico Kötschau:

  1. Simulation of Freight Transportation with Heterogeneous Vehicles in MATSim
  2. 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.
  3. 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.
  4. 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.
  5. Robust Vehicle Routing Problem: Practical Relevance and an Overview of Optimization Problems
    • Hu, C., Lu, J., Liu, X., & Zhang, G. (2018). Robust vehicle routing problem with hard time windows under demand and travel time uncertainty. In: Computers & Operations Research, 94, 139-153.

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