Available Master's Projects

The normal prerequisite for all projects below is that you are a student doing your master's in computer science at Delft including at least the course on Advanced Algorithms and one other course in our group (e.g. the course on Algorithms for Planning and Scheduling gives a very good preparation for doing your final research project). If you are doing your master's at Delft in another subject, please additionally send us a motivation why you would like to make your thesis with our group.

Below you can find a representative sample of projects. The first table lists mostly applied projects (often with an external collaboration), and the second table more fundamental projects and their source (typically industry or a scientific collaborator). If you are interested in one of these projects and meet the criteria above, please get in touch with the respective contact person in the Algorithmics group.

Applying algorithms to real-world problems

Many exciting Master's projects can be defined by applying state-of-the-art planning, scheduling, optimization and game-theoretical algorithms to real-world problems.

Topic

Collaborator

Contact

Dynamic, temporal planning on ships of the Royal Netherlands Navy

possible internship at TNO (The Hague)

de Weerdt

Optimizing a heat network, e.g. at the TU Delft campus, from MijnWater, or for greenhouses

T. Keviczky (3ME), possible internship at Eneco / AgroEnergy / Uniper

de Weerdt

Portfolio hedging – Optimizing forward market positions or scheduling flexible charging of electrical vehicles

possible internship at Uniper or Jedlix (Eneco)

de Weerdt

Combined work and study on decision support at KLM

possible internship at KLM

de Weerdt

Quality personal transport for elderly and disabled people

possible internship at Transvision

de Weerdt

Multi-objective maintenance planning

possible internship at NS/NedTrain

de Weerdt/Witteveen

Comparing centralised and decentralised optimisation for dynamic pickup and delivery

KU Leuven

de Weerdt/Yorke-Smith

Power to Heat – Optimize use of industrial electric boiler

possible internship at Uniper

Spaan

Airline operation optimization with uncertainty

Aerospace Engineering

Spaan

Traffic flow optimization

Delft Transport Institute

Spaan

Fleet management at DEAL services

DEAL services, R. Negenborn (3ME)

Spaan

Prediction of material malfunctions in trains

possible internship at NS

Witteveen

Learning intent in object recognition

possible internship at major car manufacturer

Witteveen/Yorke-Smith

Fleet and vehicle routing with constraints

American University of Beirut

Yorke-Smith

Game-theoretic versus agent-based planning of maritime container processing

Port of Rotterdam

Yorke-Smith

Hybrid optimisation for electroplate assembly lines

American University of Beirut

Yorke-Smith

Simulation of urban dynamics under migration

possible internship with University College London

Yorke-Smith

Fundamental challenges in algorithms

A scientifically-important Master's project can come from addressing a fundamental algorithmic challenge.

Topic

Collaborator

Contact

Multi-party multi-issue negotiation versus matching

C. Jonker (Interactive Intelligence, EWI)

de Weerdt

Voting with incomplete preferences

University of Southampton

de Weerdt

Multi-objective mechanism design

P. Bosman (CWI)

de Weerdt

Auctions and tenders with uncertainty

possible internship at Grontmij

de Weerdt

Parallel algorithms for Simple Temporal Planning

L. Planken

de Weerdt

Experimental comparison of algorithms for (temporal) planning

L. Planken

de Weerdt/Yorke-Smith

Matching uncertain demand and supply in smart electricity networks

E. Walraven

Spaan

Reinforcement learning for transportation, traffic and logistics

E. Walraven

Spaan

Combining Simple Temporal Networks and Markov Decision Problems

Spaan/Yorke-Smith

Influence of behavioural factors in maritime customs processes

F.J. Srour (Lebanese American University)

Yorke-Smith

Scheduling with disjunctions and preferences

B. Venable

Yorke-Smith

Details on Selected Projects

Decision support for large-scale trip planning

Kees is a planner at a company that moves vehicles around the Netherlands. Each day, the company’s customers inform him about jobs that need to be performed the next day. A job might be to pick up a vehicle between 9:00 and 10:00 in Amsterdam and deliver it to Breda before noon. The company has drivers in many locations at its disposal and Kees has to decide to which driver each job should be allocated. As jobs typically don’t take the whole day, Kees usually assigns trips consisting of multiple jobs to a driver. Drivers use public transport to get from the dropoff location of one job to the pickup location of the next one. What’s important to Kees is to minimize the total cost of executing all jobs on a day, taking into account the hourly wages of the drivers as well as their public transport expenses. How can we create a planning algorithm for this problem to base a decision support system for Kees on?

Contact: Matthijs Spaan

Agent-based Airport Surface Traffic Planning Under Uncertainty

The amount of traffic at airports increases with every passing year. One of the major challenges which airports face is how to manage ground movements of aircraft in an effective and efficient way.  Traditionally, to address the challenge of optimal airport surface traffic planning, approaches from the area of Operations Research have been used. However, such approaches have scalability issues with handling realistic amounts of traffic at large modern airports.  In this project, agent-based planning approaches will be attempted to address the scalability issue. In contrast to traditional OR techniques, in multi-agent systems planning is performed by a large number of agents, which possess local information about the system and are able to communicate with each other.

The aim of the project is to develop an agent-based airport surface traffic planning algorithm in the context of a case study at a real airport (e.g., Schiphol).  The algorithm should be able to deal with uncertainties, in particular in input parameters such as the expected landing time of aircraft and the expected pushback time of aircraft. The planning should ensure the scheduled runway arrival times for each departing aircraft.

This project is a collaboration with Aerospace Engineering. Contact: Matthijs Spaan

Planning under uncertainty

Planning under uncertainty is a technique for enabling agents to successfully plan their decisions in domains with stochastic transitions and partial observability, e.g., because of noisy sensors. In the Artificial Intelligence community the Partially Observable Markov Decision Process (POMDP) and related multiagent models have become popular choices to address these hard planning problems. In the Algorithmics group a considerable body of expertise, algorithms and software is present regarding these methods. This allows for defining challenging projects focused on improving state-of-the-art algorithms with a high potential for publications.

Contact: Matthijs Spaan

Quality personal transport for elderly and disabled people

Currently the quality of personal transport for elderly and disabled people is very low. The main reason is the heavy competition for the three-year long contracts with governmental institutions. What if people can choose their preferred company per ride? What if people can choose between different pick-up times for their transport?

In this project you will test multiple methods for assigning transportation jobs to taxi companies, such that the preferences and quality for the end-users increases without increasing the costs for the government too much.

Contact: Mathijs de Weerdt

Prediction of material malfunctions in trains

Modern trains are equipped with a lot of ICT supplies. This makes it possible to obtain sensor data, which can be used for data-mining. In this masters project the goal is to find, within this sensor data, indicators for (soon to be) malfunctioning material. These indicators can then be used to give advice regarding which parts of the train should be inspected and/or repaired.

This thesis consists of two parts:

  • Construct, using patterns in the sensor data and repair history, a decision model which shows what parts of the train should be inspected.
  • Refine the constructed model in order to use it for prediction of how long it will take before a part will malfunction.

Contact: Cees Witteveen

Matching uncertain demand and supply in smart electricity networks

Integration of renewable energy in power systems is a potential source of uncertainty, because renewable generation is variable and may depend on changing and highly uncertain weather conditions. An example is power generated by wind turbines. Although renewable wind energy is clean and cheap, it may be intermittent and its availability is uncertain and difficult to predict. To reduce peak power consumption and to mitigate the effects of uncertain renewable power supply, electricity usage can be deferred in time such that demand and supply are balanced. An example is the charging process of an electric vehicle, which often does not have to be charged immediately, as long as the available power in the battery is sufficient to reach a destination. In our group we develop efficient planning techniques to automatically coordinate deferrable loads, such that electricity is used when renewable supply is available.

Various MSc projects can be formulated in this domain. Examples include, but are not limited to, coordination of charging electric vehicles, scheduling household appliances, electricity usage within network constraints and predicting electricity demand and supply. We also have an existing collaboration with the Electrical Sustainable Energy department of the EEMCS faculty.

Contact: Erwin Walraven, Matthijs Spaan

Reinforcement learning for transportation, traffic and logistics

Reinforcement learning is a branch of machine learning focusing on agents that are able to automatically learn how they should act in their environment, and has been applied for coordination of multiagent systems in several real-world domains. For instance, reinforcement learning has been used for intelligent control of multiple traffic lights in the urban area, as well as optimization of traffic flow on highways. Other examples in the area of transportation and logistics include air traffic management and automated unloading of ships in a harbor. Typically, a real-world problem is modeled as reinforcement learning problem and then evaluated through realistic simulations.

MSc projects may focus on the application of reinforcement learning algorithms to realistic domains, but also allow for more fundamental research related to reinforcement learning and decision making under uncertainty. In our group expertise and software are present for reinforcement learning in transportation, traffic and logistics domains. However, students may also choose their own problem domain to define an interesting thesis project.

Contact: Erwin Walraven, Matthijs Spaan

More Information

The topics listed above are only a sample of possible projects; more up-to-date information can be obtained by contacting our staff members:

If you are currently not studying in Delft, but would like to study here, you will find TU Delft a stimulating academic and social environment. To apply for a MSc, please read and follow the formal guidelines of Delft University of Technology.