Invited Talks

Plenary Speakers

Prof. Dr. John R. Birge

Hobart W. Williams Distinguished Service Professor of Operations Management

The University of Chicago Booth School of Business, United States


Title of Talk: Lessons for OR from the COVID-19 Pandemic



The COVID-19 pandemic has had a significant impact on all aspects of human society over the past two and a half years. In particular, researchers in many academic disciplines, including operations research, have devoted substantial effort to understanding various aspects of analyzing and controlling epidemics. These investigations have revealed new dimensions of the disciplines themselves as well as the underlying health phenomena. This talk will consider some of these lessons for operations research and its role in addressing issues for global policies.


John R. Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. He studies mathematical modeling of systems under uncertainty, especially for maximizing operational and financial goals using the methodologies of stochastic programming and large-scale optimization. He has applied his work in a variety of contexts and industries including energy, finance, health care, manufacturing, and transportation. He has published widely and is the recipient of the Best Paper Award from the Japan Society for Industrial and Applied Mathematics, the Institute for Operations Research and the Management Sciences Fellows Award, the Institute of Industrial Engineers Medallion Award and was elected to the National Academy of Engineering.

A former dean of the Robert R. McCormick School of Engineering and Applied Sciences at Northwestern University, he has worked as a consultant for a variety of firms including the University of Michigan Hospitals, Deutsche Bank, Allstate Insurance Company, and Morgan Stanley. Birge earned a bachelor's degree in mathematics from Princeton University in 1977 and a master's degree and a PhD in operations research from Stanford University in 1979 and 1980, respectively.


PD Dr. Victor Pankratius

Director and Head of Global Software Engineering

Bosch Sensortec


Title of Talk: Sensing Applications as a Driver for Edge-AI Solutions



New generations of sensors are increasingly equipped with microcontrollers and computing capabilities that enable local machine learning in millimeter-sized packages. This talk presents examples and use cases where sensing applications have become a major driver for artificial intelligence in ultra-low power contexts at the edge. Applications are shown for intelligent Micro-Electro-Mechanical Systems (MEMS) in motion learning in mobile & wearable devices, work ergonomics, sports analytics, as well as gas and environmental sensing. Supply chain optimizations will be able to leverage smart sensors equipped with enhanced inertial navigation where GPS is unavailable, thus enabling new capabilities in asset tracking for billions of assets. Looking at the software stack, this talk also addresses the importance of formalizing and including domain knowledge into machine learning as an additional lever for optimizations, such as shrinking memory footprints, making trade-offs in signal processing, and selecting algorithms. Learning from individual success stories, our insights help sketch a bigger picture for technology ecosystems and platforms that are beginning to take shape, and how various groups and communities can be engaged.


Victor Pankratius currently serves as a Director and Head of Global Software Engineering at Bosch Sensortec. He is an experienced department lead with MIT and NASA research background in artificial intelligence, software engineering, and parallel computing. The global department he directs at Bosch is breaking new ground in sensor software and Edge-AI in mobile devices, wearables, automotive, AR/VR, drones, IoT, gaming, imaging, industrial, and environmental applications. Fun fact: The software for the inertial sensor on the Mars Ingenuity Helicopter - the first helicopter to fly on another planet- also comes from his department. Victor also serves as a CEO-appointed digital transformation lead across all functions of the company.

Prior to Bosch, Victor has led a data science group at MIT focusing on computer-aided discovery and advancing Artificial Intelligence methods that leverage domain knowledge. He served as a principal investigator in NASA's prestigious Advanced Information Systems Technology program. He also co-authored two of the first articles on imaging the event horizon of a black hole; advanced automated landing site selection on Mars and on the Moon; developed computer-aided discovery methods for planets beyond our solar system, as well as for Earth observation and geophysical phenomena (e.g., volcanoes, earthquakes, groundwater, ionosphere). Victor’s software has been included in the Arctic Vault, a project aiming to preserve information 250 meters deep in permafrost for one thousand years.

Victor earned a Habilitation degree (~'2nd PhD') in Computer Science from the Karlsruhe Institute of Technology, Germany, where his work advanced methodology and tools in software engineering and parallel systems programming, as well as the application of machine learning in software development and debugging. From the University of Karlsruhe he earned from the business school a doctorate in business with distinction (Dr.rer.pol.,'1st PhD'), focused on digital products and product lines for digital products. From the University of Münster, Germany, he received a Diplom degree (M.S.) in Business Computer Science best of class and a Bachelor of Science in Information Systems (BScIS).


Prof. Dr. Maria Grazia Speranza (The EURO Plenary)

Full Professor of Operations Research

University of Brescia, Italy


Title of Talk: Optimization in transportation and logistics: yesterday, today, tomorrow



Technological changes have been dramatic in the last decades and are changing the way people move and goods are transported. The Internet of Things (IoT) makes objects and places capable of receiving, storing and transmitting information. On the other hand, sustainability is a challenge for institutions, companies, researchers Coordination opportunities are enormous. A systemic approach to problems and advanced analytical methods are even more vital than in the past. In this talk, starting from the research carried out in the past, the main trends in the use of optimization models for problems in transportation and logistics will be presented and some research directions will be discussed.


M. Grazia Speranza is full professor of Operations Research at the University of Brescia, where she served as Dean of the Faculty of Economics and Business and Deputy Rector. She is a former President of IFORS (International Federation of the Operational Research Societies), of EURO (association of European Operational Research Societies) and of TSL (Transportation Science and Logistics society of INFORMS). As EURO President she founded the EURO Journal on Transportation and Logistics, the EURO Journal on Computational Optimization and the EURO Journal on Decision Processes.

Grazia’s research focuses on mixed integer programming and combinatorial optimization with applications to transportation, supply chain management, scheduling and portfolio selection. She models real problems, designs exact and heuristic algorithms, and applies worst-case analysis to off-line problems and competitive analysis to on-line problems. Recent research is oriented towards the study of routing problems enabled by technological developments and of innovative transportation systems.

Grazia is author of more than 200 papers that appeared in international journals and volumes. Her papers have received more than 13000 citations with an h-index of 62, according to Google Scholar. She has been plenary speaker at several international conferences and member of the scientific committee of the major international conferences in the field. She was visiting professor at the London School of Economics and at Brunel University during her sabbatical and has given talks and seminars at many universities around the world. She has been guest editor of special issues of journals, editor of several international journals and is co-editor-in-chief of the series of books ‘EURO Advanced Tutorials in Operational Research’.

Grazia has been a member of many evaluation committees and panels, including the European Research Council (ERC) mathematics panel. She is included in and in the book ‘100 donne contro gli stereotipi per la scienza', Egea, 2017 as one of the best 100 Italian women in the STEM area. In 2019 she was awarded with the Laurea honoris causa by the University of Freiburg, Switzerland. She is a member of the Academy of Sciences of the University of Bologna.


Semi-Plenary Speakers

Prof. Dr. Gabriele Eichfelder

Full Professor for Mathematical Methods of Operations Research

Institute of Mathematics of the Technische Universität Ilmenau, Germany


Title of Talk: Algorithmic Developments in Multiobjective Optimization



In multiobjective optimization one considers optimization problems with several competing objective functions. Such problems arise in a large variety of applications. They play, for instance, an important role in models of the energy market when intelligent neighborhood networks have to be integrated in overarching distributing networks and a variety of individual and competing criteria have to be taken into account. Next to multiple objective functions, these problems often possess additional difficulties as discrete-continuous variables, bilevel structures, or uncertainties.

In this talk we give an introduction to multiobjective optimization. We shortly present the most widespread solution approach which is known under the name scalarization. There, one formulates parameter-dependent single-objective replacement problems and solves those iteratively for a set of parameters. We discuss the limits of such approaches in terms of quality guarantees and for specific classes of optimization problems as those with additional difficulties as mentioned above. Moreover, we give some of the basic ideas of direct methods which avoid to scalarize first and, thereby, try to overcome some of these issues.   


Gabriele Eichfelder is a full Professor for Mathematical Methods of Operations Research at the Institute of Mathematics of the Technische Universität Ilmenau, Germany. She earned her Doctoral degree in 2006 and completed Habilitation at the Department of Applied Mathematics at the University of Erlangen-Nuremberg, Germany in 2012. She works in the field of Mathematical Optimization with a special interest in nonlinear optimization with vector-valued and set-valued objective functions. In addition to fundamental theoretical studies, she has also been working on numerical solvers for applied engineering problems. Among others, her current research projects deal with nonlinear mixed-integer problems containing multiple objectives and with handling uncertainties in a robust optimization within energy networks.

Professor Eichfelder has authored two Springer research monographs and published extensively in top international journals. Recently, she served as a member of the programme committee of the XXXI EURO Conference 2021 in Athens and was regularly a member of the programme committee of EUROPT workshops on advances in continuous optimization. She was a member of the organizing committee of the 2021 SIAM conference on optimization, invited cluster chair for the international conference on continuous optimization ICCOPT 2019 in Berlin, and invited stream organizer at the OR2019 in Dresden. Currently, she serves on the editorial boards of Computers & Operations Research, EURO Journal of Computational Optimization, Operations Research Letters and Optimization, and as an area editor of the Journal of Optimization Theory and Applications.


Prof. Dr. Volker Kaibel

Chair for Mathematical Optimization

Otto von Guericke Universität Magdeburg, Germany


Title of Talk: Steiner Cut Dominants



For a subset of terminals T of the nodes of a graph G a cut in G is called a T-Steiner cut if it subdivides T into two non-empty sets. The Steiner cut dominant of G is the Minkowski sum of the convex hull of the incidence vectors of T-Steiner cuts in G and the nonnegative orthant. It is the polyhedron that is naturally associated with the problem of finding a minimum T-Steiner cut in G w.r.t. nonnegative edge weights. While it is well understood for two terminals (s-t-cuts), for larger sets T no inequality descriptions have been known so far despite quite some efforts that have been spent into investigating this problem for T being the complete node set of G (global cuts). In this talk we derive such descriptions for all graphs and up to five terminals. Moreover, we prove that for each number k there is a finite list of inequalities from which one can derive by means of iterated applications of two simple operations inequality descriptions of the Steiner cut dominant for every graph and up to k terminals. We furthermore introduce the concept of the Steiner rank of a facet of  a global cut dominant and classify the facets of Steiner rank at most five. Via blocking duality those results  yield corresponding  results for the vertices of the subtour elimination polytope that is most relevant in the context of the traveling salesman problem.

The talk is based on joint work with Michelangelo Conforti (U Padova).


Volker Kaibel holds a chair for Mathematical Optimization at Otto-von-Guericke Universität Magdeburg and is the vice spokesperson of the DFG-Graduiertenkolleg 2297 “Mathematische Komplexitätsreduktion“ at that university. He received his doctoral degree from Universität zu Köln in 1997 and his habilitation from Technische Unuiversität Berlin in 2002. He has been the deputy head of the Department of Optimization at Zuse Institute Berlin from (2005-2006) and a visiting professor at TU Berlin (2006).

Volker Kaibel's research interests are located in the areas of discrete optimization and geometry with a special focus on polyhedral combinatorics. He currently serves as a coeditor for Mathematical Programming, Ser. A, and has previously served as associate editor for several journals, as the area editor for Mixed Integer Optimization of Operations Research Letters (2014-2019) and as the editor of the newsletter Optima of the Mathematical Optimization Society (2014-2018). He has been a member of the council-at-large of the Mathematical Optimization Society (2012-2015)


Prof. Dr. Bahar Yetiş Kara

Professor in the Department of Industrial Engineering 

Bilkent University, Turkey


Title of Talk: Network Design in Humanitarian Logistics



Emerging humanitarian crises of the last decades demonstrated the necessity to excel in the management of time constraints, limited resource amounts and high levels of uncertainty. Syrian refugee crisis that has been going on for more than a decade illustrates one of the main characteristics of humanitarian logistics: selectivenes. Management of scarce resources do not always allow for the coverage of all of the demand. Dynamics of location decisions and selectiveness characteristics highlight the fundamental importance of network design in humanitarian logistics.

Recent outburst of COVID-19 pandemic show that quick responses to humanitarian crises is essential. The problems of fast distribution of masks, PCR tests and vaccines show that humanitarian crises are rapid changing situations where new needs and questions arise continuously. Scarcity of resources of time and supply in a crises that require quick response also obligate selectiveness in terms of the demand that can be covered. Current situation in Ukraine shows that, unfortunately, there will always be emerging humanitarian crises in the world and Network Design in Humanitarian Logistics will continue to be a critical research topic in the years to come.

In this talk, a general framework for network design in humanitarian applications will be provided and examples from different applications will be discussed. The examples analyzes Turkey’s experience and lessons learned over a decade to provide a road-map for possible and similar situations in the future.


Bahar Yetis Kara is a professor in the Department of Industrial Engineering at Bilkent University where she has been a faculty member since 2001. Dr. Kara holds an M.S. and Ph.D. degree from Bilkent University Industrial Engineering Department, and she worked as a Postdoctoral Researcher at McGill University in Canada. Since 2021, she has served as Department Chair at Bilkent University Industrial Engineering Department.

Dr Kara’s current research interests include distribution logistics, humanitarian logistics, hub location and hub network design. She is currently an associate editor of IIE Transactions, Transportation Research B, JORS, and SEPS. Dr. Kara is also one of the founders and a member of the executive board of the EURO Working Group on Humanitarian Operations (HOpe). She is also a member of the executive board of Bilkent University and of Turkish OR Society. Dr. Kara editored 4 books and authored/coauthored more than 80 journal and conference papers. She has been a doctoral advisor to 7 PhD dissertations, 2 of which are still ongoing. Dr. Kara has also supervised 30 masters theses, 29 of them are successfully defended.

Since 2001, Dr. Kara has received 10 research grant fundings from many academic and industrial organizations including The Scientific and Technological Research Council of Turkey (TÜBİTAK), The Inter Academy Panel (IAP), and United Kingdom Research and Innovation. Dr. Kara holds “Best Dissertation Award” given by INFORMS-UPS-SOLA (2001), TUBA-GEBIP (National Young Researchers Career Development Grant) reward (2008), IAP Young Researchers Award (2009), and TÜBİTAK Young Scientist Incentive Award (2010). One of her PhD graduates, Dr. Sibel Alamur, received the Chuck ReVelle Rising Star Award given by INFORMS-SOLA in 2017


Prof. Dr. Nadia Lahrichi

Full Professor in the Department of Mathematical and Industrial Engineering

Polytechnique Montréal, Canada


Title of Talk: Improving Cancer Treatment Logistics



The main cancer treatments are surgery, radiation therapy and chemotherapy. The complexity of the logistical process of scheduling treatment appointments stems from the fact that it involves extremely costly resources, sometimes synchronously. Several due dates (i.e., appointments already scheduled, maximum wait times) and unexpected events such as the arrival of patients requiring urgent palliative care add to the difficulty. This talk will investigate how can simulation and optimization models help improve the efficiency of cancer treatment centers and share experiences on patient booking, physician scheduling, and capacity assessment. All projects are conducted in close partnership with hospitals and rely on real data.


Nadia Lahrichi is Full Professor at the Department of Mathematical and Industrial Engineering in Polytechnique Montréal in Quebec, Canada. Her area of interest are mainly focused towards applying modeling and operational research tools in healthcare especially targetting patient flow and resource optimization.  She is a member of Institute for Data Valorization (IVADO), Tier-1 Canada Research Chair in Analytics and Logistics in Helathcare (HANALOG), and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT).


Assist. Prof. Dr. Virginie Lurkin

Assistant Professor in the Department of Operations at the Faculty of Business and Economics

University of Lausanne, Switzerland


Title of Talk: Moving Consumer Goods, Not Vehicles



The Vehicle Routing Problem (VRP) is certainly the most widely studied combinatorial optimization problems within the transportation research community. Simply defined it aims at finding the least cost delivery routes to visit a set of geographically dispersed customers. A very large variety of formulations and solutions methods have been proposed since Dantzig and Ramser pioneered the field in 1959.

Over the last decade, the ever-accelerating trends of e-commerce and urbanization led the logistics community to define rich routing problems, embedding new attributes to better represent real-world environments. Although the objective of smooth and seamless flow of goods in urban areas remains, more sophisticated VRP formulations have been introduced to capture the complexity of existing problems more accurately. Well-known examples are the inclusion of environmental concerns or uncertain traffic conditions.

While all VRPs require demand data as input, the logistics community has mainly focused on the operational objective and constraints of the supplier, whereas the behavior of the consumers has been traditionally implicitly addressed in the planning process. This is still the case for most rich VRP. Aggregation or simplifying assumptions are made on the demand side. In this talk, I discuss how disaggregate demand representations allows to better account for the heterogeneous preferences of the customers (for example regarding delivery time and location). Using city logistics examples, I show how to embed disaggregate demand assumptions within routing problems. While doing so allows to better reflect the supply-demand interactions within the system, it also leads to hard optimization problems, for which suited solutions methods need to be designed.


Virginie Lurkin is an assistant professor in the Department of Operations at the Faculty of Business and Economics, University of Lausanne. Her areas of expertise are sustainable urban mobility and logistics. Her research aims at developing innovative solutions for sustainable urban mobility and logistics, using multidisciplinary methodologies rooted in operations management, discrete choice models, and datadriven decision making. Before joining HEC Lausanne, Virginie Lurkin was an Assistant Professor at Eindhoven University of Technology in the Netherlands, within the group Operations, Planning, Accounting and Control (OPAC). She completed a postdoc at the Transport and Mobility Laboratory at EPFL and a PhD in Economics and Management Sciences at HEC-Liège in Belgium. During her PhD, she was also a visiting scholar at the Georgia Institute of Technology in Atlanta. Her dissertation won the 2016 “INFORMS Aviation Applications Dissertation Prize”


Prof. Dr. Stefan W. Pickl

Univ.-Professor in the Institute for Theoretical Computer Science, Mathematics and Operations Research

Universität der Bundeswehr München, Germany


Title of Talk: 50 Years Club of Rome - OR Challenges and Perspectives on Energy Security and Complex Resource Conflicts



2022 is a special year from different perspectives. Resource conflicts, security, and climate-policy issues play an important role. This talk summarizes the history of 50 years of the Club of Rome and presents personal views from Operations Research on complex resource conflicts and scenario-based decision-making processes in the context of energy security.

Different mathematical decision models and solution concepts are  presented. The TEM model is summarized and a game-theoretic extension is discussed. An algorithmic solution concept based on intelligent optimization techniques is derived. Some generalizations are characterized.

In the future, managerial decision-making will be influenced by certain developments of AI-based expert systems, machine learning techniques, as well as various reinforcement learning approaches. Prescriptive analytics could be considered as an example of how managerial decision-making could be seen as a further application for control science  and classical optimization in the context of energy security and complex resource conflicts.

Can intelligent optimization lead to sustainable solutions … ?

This contribution is dedicated to Ernst Ulrich von Weizsäcker, Honorary President of the International Club of Rome.


Stefan Wolfgang Pickl is a professor at the Institute for Theoretical Computer Science, Mathematics and Operations Research in Universität der Bundeswehr München, Germany. He studied mathematics, theoretical electrical engineering and philosophy at the TU Darmstadt (diploma 1993, ERASMUS scholarship holder at the EPFL Lausanne)where he also earned his doctorate in 1998. In 2004 he got C4 professorship for operations research at the University of the Federal Armed Forces in Munich and earned Habilitation in 2004/05 from the University of Cologne. From 2000-2005, Mr. Pickl was a research assistant and project manager at the Center for Applied Computer Science in Cologne (ZAIK) with main responsibility in the area of "Modeling, Simulation and Optimization of Resource Conflicts - Analysis of Complex Systems". Since July 2005 he has officially held the Chair for Operations Research at the Bundeswehr University in Munich.


Dr. Thomas Stützle

Research Director

The Belgian F.R.S.-FNRS (National Science Foundation)


Title of Talk: Automated Design of Algorithms



The design and development algorithms can be time-consuming and difficult for a number of reasons including the complexity of the problems being tackled, the large number of degrees of freedom when designing an algorithm and setting its numerical parameters, and the difficulties of algorithm analysis due to heuristic biases and stochasticity. Still very often this design is done manually, mainly guided by the expertise and intuition of the algorithm designer. However, the advancement of automatic algorithm configuration methods offers new possibilities to make this process more automatic, avoid some methodological issues, and at the same time improve the performance of algorithms.

In this talk, I will highlight the advantages of addressing algorithm design and configuration by algorithmic techniques; describe the main existing automated algorithm design techniques; and discuss some of the main successful applications of automated design we have in our own work. In particular, I will show how flexible algorithm frameworks can support the automated design of high-performing hybrid stochastic local search algorithms. In fact, even for problems that have received very high attention in the literature new state-of-the-art algorithms can be obtained automatically, that is, without manual algorithm tuning. I will conclude arguing that automated algorithm design will also have the power to transform the way algorithms for difficult problems are designed in the future.


Thomas Stützle is the Research Director of the Belgian F.R.S.-FNRS (National Science Foundation) working at the IRIDIA laboratory of Université libre de Bruxelles (ULB), Belgium. He received the Diplom (German equivalent of MSc. degree) in business engineering from the Universität Karlsruhe (TH), Germany in 1994, and his PhD and habilitation in computer science both from the Computer Science Department of Technische Universität Darmstadt, Germany, in 1998 and 2004, respectively.

He has co-authored three books among which are “Stochastic Local Search: Foundations and Applications” (Morgan Kaufmann) and “Ant Colony Optimization” (MIT Press), both being the main references in their respective areas. His other publications include more than 250 articles in journals, international conferences or edited books many of which are highly cited. In fact, his research contributions received so far more than 50,000 citations in Google Scholar and his h-index is 76. His main research interests are in stochastic local search algorithm, swarm intelligence, multi-objective optimization, and automatic design of algorithms. He is probably best known (i) for his contributions to early advancements in ant colony optimization including algorithms such as Max-Min Ant System, (ii) the establishment of algorithmic frameworks for iterated local search and iterated greedy, and (iii) as a driving force in the advancement of automatic algorithm configuration techniques and their usage in the automatic design of high-performing algorithms.

He received seven best paper awards from conferences and his 2002 GECCO paper on “A Racing Algorithm for Configuring Metaheuristics” received the 2012 SIGEVO impact award. He is an Associate Editor of Applied Mathematics and Computation, Computational Intelligence, Evolutionary Computation, International Transactions in Operational Research, and Swarm Intelligence and on the editorial board of seven other journals. He is also frequently involved in international conferences and workshops with program or organizational responsibilities.


Prof. Dr. Tjark Vredeveld

Professor of Planning and Scheduling QE Operations Research, Quantitative Economics

School of Business and Economics at Maastricht University, The Netherlands


Title of Talk: Additive Approximation and Approximation Schemes for Load Balancing



Many applications in discrete optimization lead to hard problems. Under common assumption, it is impossible to find an algorithm that (1) is efficient, (2) finds an optimal solution on (3) every instance. At least one of these requirements needs to be sacrificed to cope with these problems. In the area of approximation algorithms, the goal is to design algorithms that efficiently find provably good solutions. Typically, for approximation algorithms, provably good implies that we bound the approximation ratio of the value of the solution to the optimal value.

One important reason for studying approximation algorithms is that often even on simplified problems, they give us insights in how to design heuristics for the real problem that needs to be solved. Furthermore, having a mathematical proof for an approximation guarantee often results in a deeper understanding of the structure of the underlying problem.

Unfortunately, in some cases finding a guarantee on the approximation ratio is impossible, e.g., when the optimal solution value is 0. Or the approximation guarantee is overly pessimistic, e.g., Graham’s (1966) seminal List Scheduling algorithm for makespan scheduling is guaranteed to find a solution with value at most twice the optimal value, but when processing times are small List Scheduling performs much better. To overcome these issues, we consider the concept of additive approximation algorithms. Instead of bounding the ratio, in additive approximation we bound the absolute difference between the value of the solution of the approximation algorithm and the optimal solution value. We apply the concept of additive approximation and additive approximation schemes, that can get arbitrarily close to an optimal solution, for several load balancing problems.


Tjark Vredeveld is a professor in Planning and Scheduling at Maastricht University School of Business and Economics. He received his PhD in mathematics from Eindhoven University of Technology in 2002. After obtaining his PhD, he has worked as a postdoctoral fellow at the University of Rome, La Sapienza and as a researcher at the Zuse Institute in Berlin. In 2005, he moved to Maastricht University, where he now is a full professor at the department of Quantitative Economics. He has been a guest professor at the Escuela Polytechnica Nacional in Quito and a visiting researcher among others at the TU Berlin and Universidad de Chile.Tjark Vredeveld is member of the executive board of the Dutch Network on the Mathematics of Operations Research (LNMB) and the board of the Mathematics Centre Maastricht.

The research of Tjark Vredeveld is in discrete optimization, especially in scheduling and resource allocation. He focusses on the complexity as well as the design and analysis of (approximation) algorithms. Moreover, he is interested in dealing with uncertainty, both in the design of algorithms as well as in the analysis of them.


Talk of the Winner of the 2022 GOR Company Award

The winner will be announced during the Opening Session on Wednesday, September 7.

Learn more about the award

Please click here to learn more about the award.