Mathijs de Weerdt

Please see my inaugural lecture (from June 7, 2023) with the title "Algorithms find solutions, so what’s the problem?" (starting at minute 26, for just the slides please see here).

Wil je wat meer over me te weten komen, maar liever in het Nederlands? Luister naar deze podcast over kunstmatige intelligentie en de energiesector (29 oktober 2020).

I am Full Professor on Algorithms for Planning and Scheduling and section head of the Algorithmics Group of the TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Department of Software Technology.

My aim is to develop new algorithmic techniques that cope with challenges prohibiting successful use of algorithmic and artificial intelligence planning and optimization techniques in practice. The most prominent fundamental challenges regarding the planning problems I am working on are:

  1. Robustness and uncertainty: we often have access to historical data and increasingly accurate predictions; still the future is uncertain, and the aim of planning algorithms is to suggest next steps/decisions that are not only good in expectation, but also allow for changes later, in case of less likely scenarios. In this line of work we build upon the fields of Stochastic Programming, Reinforcement Learning, and AI Planning (e.g. plan repair algorithms).
  2. Scalability: considering possible future decisions and effects of these increases the theoretical complexity of these already NP-hard problems. State-of-the-art algorithms have runtimes that are exponential in the number of actions/decisions, the horizon, etc., which makes it impossible to use them in practical settings. Furthermore, sometimes there are non-linear relations further complicating the planning and optimization problem. Here we extend and combine techniques from the fields of Operations Research (e.g. Benders decomposition, column generation), Constraint Programming, Decision Diagrams, Evolutionary Algorithms, Surrogate Modelling and Machine Learning.
  3. Multiple parties: although this is the main approach taken in Operations Research, in practice decision-making is never done in isolation by a single entity with a clear single objective: preferences and information from multiple parties are relevant for the decisions and therefore decisions should not only by cost-efficient but also fair and taken such that self-interested parties have little opportunities to manipulate them. Another related perspective is that all the time many parties make decisions (distributedly) that influence each other and together determine the efficiency/fairness of the system or society. To make progress here we study and extend fundamental research from the fields of Algorithmic Mechanism Design, Social Choice and Multiagent Systems (e.g. multi-agent path finding).

I really enjoy working on these challenges in the context of other science or engineering fields, evaluating my ideas in practice or close-to-real-world simulations. The benefits of confronting state-of-the-art fundamental algorithms with realistic problem models (often together with colleagues from other fields or the industry) is three-fold:

  1. it provides inspiration, context and motivation for fundamental open problems
  2. it helps me to acquire relevant data and provide realistic benchmarks to the broader scientific community
  3. by doing this I can make an impact in practice

Mainly I work on such algorithmic challenges related to the energy transition and in efficient transportation, but also on planning and scheduling problems regarding satellite scheduling and harvesting and logistics of soft fruit chains.

  • For example, moving towards sustainable energy, the production costs of electricity become dependent upon the weather. Users planning electricity-consuming activities have individual objectives that are different from those of electricity generators and network operators. As a society we are interested in a pricing scheme for electricity with an equilibrium for electricity consumption and production that optimizes the social welfare. Furthermore, we need algorithms to schedule flexible loads in this uncertain context. To facilitate the development of such algorithms, we have developed an open-source benchmark/simulator called B-FELSA. Please refer to the respective publication on Benchmarking Flexible Electric Loads Scheduling Algorithms.
  • In the context of transportation, I'm working closely with the Dutch railways (Nederlandse Spoorwegen) on algorithms for train unit shunting and scheduling of servicing. An important objective for me here is to invite the scientific community to contribute to solving this complex logistic problem by setting up a scientific competition. I'm especially interested in benchmarking different algorithmic techniques (developed by different communities) such as multi-agent path finding algorithms, constraint programming, reinforcement learning, and mathematical programming. Research into this complex logistical problem of train shunting and servicing is made possible by our open-source simulator called TORS. This has been first published in a paper in the demo track at AAMAS.

Within the Algorithmics group we bring together researchers with specific algorithmic and artificial intelligence expertise. We support each other in project acquisition, and complement each other's expertise in executing projects and supervising phd students.

Within the TU Delft organisation, I aim to more broadly facilitate the use of state-of-the-art algorithmic and AI techniques in practice, and in other sciences, please see the AIDU website for the current status of our efforts.

If you're interested in joining me in my quests, please drop me an email.

Mathijs de Weerdt is full professor on Algorithms for Planning and Scheduling and section head of the Algorithmics Group at Delft University of Technology. After his PhD he received the prestigious VENI grant from NWO to support his research into coordinated planning from 2005 to 2008. He has been a visiting researcher at the Dutch Center for Mathematics and Computer Science (CWI) from 2005 to 2016, Cork in 2006, Southampton between 2012 and 2015 and Duke in 2017. Mathijs currently is scientific advisor at the Dutch national railways (NS), and one of the founders of the TU Delft Rail Institute (2020). He has been and is (co-)promotor of in total 15 PhD students of which 9 have completed their PhD thesis so far. An important challenge for him is to identify how Artificial Intelligence can contribute to the energy transition and sustainability of our society, and what is needed to speed-up such developments. This is a main driver of his research, but also an important topic in the convergence discussions with Rotterdam and Leiden universities. He is chairman of the working group on Energy & Sustainability of the Dutch AI coalition (NLAIC) (e.g. see event "meet the researcher" in 2020).


  • 1998-2003 PhD Student at Delft University of Technology on Algorithms for Plan Merging in Multi-Agent Systems
  • 1994-1998 BSc and MSc., Computer Science, Utrecht University, Intelligent Systems/Algorithmics (cum laude)

Grants as main applicant

  • 2021  NWO ESI-FAR: Next Generation Sector-Coupling Models for Optimal Investments and Operation
  • 2020  RVO & TKI TU: Een optimale zacht-fruitketen begint bij de oogst
  • 2016  NWO-URSES+: Future-proof Charging 
  • 2016  NWO ESI-pose: Flexibility in Industry
  • 2014  NWO-URSES: Gaming beyond the copper plate
  • 2009  Basic-NGI: Dynamic contracting in infrastructures
  • 2004  NWO/STW-VENI national personal grant

Grants as co-applicant

  • 2021 AI4Bio ICAI Lab with DSM
  • 2019  RVO Urban Energy: Flex-DC
  • 2019  RVO Urban Energy: Flex met warmte
  • 2017  NWO Big data: Real-time data-driven maintenance logistics
  • 2016  ERANET Smartgrid Plus: DCSMART
  • 2014  AMS Stimulus: From Needs to Knowledge, challenges and opportunities for city-scale crowd sensing in intelligent cities (small grant)
  • 2012  TopSector Energy: Smart Energy Systems, WarmteWeb, with Eneco
  • 2011  IJCAI Extended Lab Visit, Jim Boerkoel, University of Michigan, Multi-agent temporal planning (small grant)
  • 2011  Faculty of EEMCS, internal, SHINE, Sensing Heterogeneous Information Network Environment
  • 2010  Marie Curie (as host): Planning under uncertainty for real-world multiagent systems

Prizes and awards

  • 2018  ICT.Open poster prize
  • 2016  Erasmus Energy Forum Science Award
  • 2015  Best teacher award in Computer Science (Delft)
  • 2014  Honorable mention Best Dissertation on Algorithms for Simple Temporal Reasoning by dr. Planken, International Conference on Automated Planning and Scheduling (ICAPS), as co-promotor and daily supervisor.
  • 2011  Honorable mention ICAPS best paper on Computing All-Pairs Shortest Paths by Leveraging Low Treewidth
  • 2011  Nomination for Best Paper, IEEE International Conference on Networking, Sensing and Control (ICNSC).
  • 2009  Nomination for Best Paper, Pacific Rim International Conference on Multi- Agents (PRIMA).


  • Chair of the ICAPS conference 2018
  • Co-chair of conference on CPAIOR 2018
  • Organizing Committee (scholarship chair) of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) in 2014
  • (S)PC member of the AAMAS conference 2008-now (SPC in 2009,2010,2012,2016-)
  • (S)PC member of the ICAPS conference 2010-now
  • (S)PC member of the IJCAI conference 2009-now
  • (S)PC member of the AAAI conference 2016-now
  • PC member of several other conferences and workshops
  • Organizer of the multiagent planning workshop (at AAMAS'07 and ICAPS'08)
  • Tutor for the EASSS summer school 2005-2008,2014 (e.g. see the EASSS'14 website for slides and Extra Material on Dynamic and Online Mechanism Design).
  • Research fellow of the Research School for Transport, Infrastructures, and Logistics (TRAIL)
  • Research fellow of the School for Information and Knowledge Systems (SIKS)
  • Board member of the Dutch Association for Theoretical Computer Science (NVTI)
  • Board member of the AgTech institute in its inaugural year (2020)
  • Coordinator of the lunch lectures for the PowerWeb institute
  • Reviewer for many journals, such as Artificial Intelligence (AI), Artificial Intelligence Review (AIRE), Applied Intelligence (APIN), Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), Annals of Mathematics and Artificial Intelligence (AMAI), IEEE Transactions on SMC, IEEE Transactions on Sustainable Computing, IEEE on Cybernetics, Journal of Scheduling (JOSH), Mathematical Social Sciences (MSS), Multiagent and Grid Systems: An International Journal (MAGS), Transactions on Internet Technology (TOIT), Transportation Research: Part C (TRC), Transactions on Economics and Computation (TEAC), Transactions on Intelligent Systems and Technology (TIST), Theoretical Computer Science (TCS), Journal of Computational Science, Journal of Artificial Intelligence Research (JAIR).
  • Grant reviewer and/or grant review committee member for WT-Flanders, Israel Science Foundation, Natural Science and Engineering Research Council of Canada, Czech Science Foundation, Engineering and Physical Sciences Research Council (UK), FP7-STREP (EU) and NWO Klein.

Valorisation and outreach


I aim to make code and data public for reproducibility and to support follow-up work. Please see here for the software and data reported in the publications of our group.

Projects and PhD students

  1. We have a vacancy in the Rail lab, co-supervised with Anna Lukina (2023-2027)
  2. 2nd promotor of Bagas Ihsan Priambodo with 1st promotor Pavol Bauer and co-supervised by Gautham Ram Chandra Mouli in a project with Shall Recharge (2023-2027)
  3. Promotor of Konstantin Sidorov in the XAIT lab, co-supervised with Emir Demirovic and Gonçalo Homem de Almeida Correia (2023-2028)
  4. Promotor of Maaike Elgersma in the NextGenOpt project, co-supervised with Karen Aardal (2022-2026) - see here for a brief video explanation of this project in Dutch
  5. Promotor of Issa Hanou in the Rail lab, co-supervised with Sebastijan Dumancic (2022-2026)
  6. Promotor of Kim van den Houten in the project, co-supervised with David Tax (2021-2025)
  7. 2nd promotor of Yoeri de Vries in the project, co-supervised with Matthijs Spaan (2021-2025)
  8. Promotor of Koos van der Linden on optimal machine learning with constraints, co-supervised with Emir Demirovic (2021-2025)
  9. Promotor of Junhan Wen in a project named An optimal soft-fruit chain starts with the harvest, co-supervised with Thomas Abeel (2020-2024)
  10. Promotor of Ksenija Stepanovic in the Flex-Heat project, co-supervised with Wendelin Bohmer (2020-2024)
  11. Promotor of Grigorii Veviurko in the DC-Flex project, co-supervised with Wendelin Bohmer (2020-2024)

I'm also in a supporting promotor role for

  1. Emir Demirovic's PhD student Maarten Flippo (2022-2026)
  2. Benedikt Ahrens's PhD student Kobe Wullaert (2021-2025)

Former Projects and PhD Students

Project without PhD students: Routing of electrical vehicles

I did a study when visiting Southampton for about 10 weeks. Among other things, this resulted in a journal publication on routing and en-route charging of electrical vehicles. See this fun video on Minimising Queues at Electric Vehicle Charging Stations produced by Sebastian Stein about our research. We also published our dataset.

In 2017 (April-May) I was a visiting scholar at Duke University with Vincent Conitzer.

As is common in AI and computer science, I publish my main fundamental contributions in the top (A*) conferences in my field: AAAI, AAMAS, IJCAI, ICAPS (see e.g. the core ranking or Google Scholar metrics). Contributions to operations research or other fields of science or engineering I publish in the respective journals. Below I've first highlighted some of my more recent publications on each of the three main challenges I'm working on. Below then can all my publications from the TU Delft research portal be found. Most are also included in the DBLP here. The website with publications of Mathijs de Weerdt at Google Scholar additionally contains an up-to-date citation count and h-index.

Fundamental contributions

Other fields of science or engineering

All publications

(ordered by year)

Bachelor courses

I'm responsible for the following bachelor courses in the core of the bachelor programme on Computer Science and Engineering in Delft.

  • Algorithm Design (responsible teacher. recently with Emir Demirovic and Stefan Hugtenburg): this second-year course is on algorithmic techniques such as greedy algorithms, divide & conquer, dynamic programming and network flow; see also my open courseware version on Algorithm Design (recordings in Dutch, slides in English). I have designed this course from scratch, replacing a course on discrete mathematics. In more recent year I've tried to make this course more accessible to a broader range of students by facilitating different study paths through the introduction of what we call skill circuits. In these, students can select their own series of tasks to train themselves. This course is also a pioneering user of the virtual programming environment developed by Eelco Visser's group, called WebLab. Using this environment, students can receive automated feedback on the correctness of the implementation of their algorithms.
  • Research Project (coordinator/responsible teacher with Gosia Migut): Based on the outcome of my 2018 curriculum-update committee work, I proposed a new course design for the final (third year, 15 EC) project of the CSE bachelor to let students learn some research skills. I have run pilots with 10, 15 and 45 students to prepare for the official 2020/2021 run of the course with close to 300 students. See also this news item from July 2021.
  • As a supervisor of one or two groups of students I'm regularly involved in the Software Project (formerly known as BEP), the Honours Program (specifically on competitive programming), and of course the Research Project as well.

Master courses

In the master I'm responsible for the courses offered by the Algorithmics group. I've delegated some of this responsibility to my colleagues, but I am still very much involved in the following master's courses.

  • Advanced Algorithms (5 EC) (responsible teacher): I coordinate this course and am responsible for teaching one half of it, about exact algorithms for NP-hard problems.
  • Algorithms for Intelligent Decision Making (5 EC) (co-teacher): I used to teach a part on algorithmic game theory until 2020.
  • Algorithms for Intelligent Decision Making Project (5 EC) (co-teacher): here I supervise some of the groups and chair some of the sessions. In the groups students execute a small research project resulting in a paper.
  • Master thesis project supervision (45 EC): I have supervised over 30 students on topics such as planning algorithms, and auction/mechanism design for multi-agent systems. I have also acted many times as an external committee member in other graduation committees. One of my students (Stranders) received the prize for the best Master’s thesis in Computer Science in 2006 from the Koninklijke Hollandsche Maatschappij der Wetenschappen for his thesis on Argumentation Based Decision Making for Trust in Multi- Agent Systems. Others worked for example in collaboration with Grontmij (now Sweco), Calendar42, ORTEC, SystemsNavigator, NS, and WithTheGrid. On average every year one of my students publishes a paper in a top venue with me based on his/her master thesis work.


  • From 2016/2017 to 2020 I have been teaching Algorithm Design for teachers in secondary education as part of Inf4All. Please see my lecture on Greedy and Dynamic Programming algorithms on YouTube (in Dutch), and the related slides of this third meeting
  • I was very active in the design of the 2018 CSE Bachelor curriculum with the following main changes: more freedom for students to personalize their program (electives in year 3), more research-oriented (e.g. the final project is now a research project), a focus on responsible computer science (throughout a number of courses), and a bit more attention to artificial intelligence.
  • I was elected teacher of the year of Computer Science Delft in 2015.
  • I was president of board of studies of Computer Science (OCI) from 2010-2014. The president sets the agenda, leads the discussions, and regularly communicates with the Director of Studies.
  • Since 2019 I'm involved in the introduction and development of more courses on Artificial Intelligence in Delft, such as the AI Technology track in the master, and an AI "mini-minor" of 15 EC for master students with a non-CS master.
  • I've given master classes on Smart Algorithms for Smart Grids for KIVI and Nyenrode/Allliander (2017).
  • I've given tutorials on multi-agent planning and mechanism design at major conferences and the European Agent Systems Summer School.
Mathijs de Weerdt

Prof.Dr. M.M. de Weerdt

Visiting Address
Building 28
Room:100 East 4th floor
Van Mourik Broekmanweg 6
2628 XE Delft
The Netherlands

Mailing Address
EEMCS, Algorithmics
P.O. Box 5031, 2600 GA Delft
The Netherlands