Faculty

Our group is part of the Department of Econometrics and Operations Research. Many of our members are also affiliated with other research institutes in the area, for instance the Tinbergen Institute and the Centrum Wiskunde & Informatica. Our group includes leading professionals in industry; we have close ties with ORTEC in particular.

Prof. Bernd Heidergott

Full Professor, group leader

I am interested in the study of stochastic phenomena. A major part of my work is on simulation optimization, and the development of unbiased gradient estimators is my specialty. During the years I maintained my interest in max-plus algebras, originating from a PostDoc period at TU Delft, and I am one of the authors of the state-of-the-art monograph “Max Plus at Work” (with over 700 citations). I am also interested in approximate numerical solutions of finite Markovian models by means of perturbation analysis and series expansions.

My recent research focuses on data driven operations research and I am particularly interested in finding ways of linking data with operations research/applied probability models, as well as the analysis of model and parameter insecurity. Application domains I am working on are financial engineering, social networks analysis, and general queuing and inventory problems.

I have published over 50 academic papers and 2 monographs. In 2008 I was the recipient of the best lecturer award of the School of Business and Economics. I am programme director of the B.S.c and M.Sc. Econometrics and Operations Research, research fellow of the Tinbergen Institute, and board member of Amsterdam Business Research Institute.

Prof. Joaquim Gromicho

Full Professor

My chair on Applied Optimization in Operations Research is very broad: the unifying aspect is optimization with sound theoretical foundation and practical relevance. I have work published on discrete, continuous, linear, non-linear, deterministic, stochastic and robust optimization both at optimal and heuristic levels. My research interests move toward optimization driven by (big) data and the convergence of mathematical optimization and artificial intelligence.

Besides the VU, I also have a position at ORTEC where my responsibilities as Scientific and Education officer focus on coordination of scientific projects and on the establishment of a corporate education program.

Prof. Guido Schäfer

Full Professor

My main research interests are algorithms and combinatorial optimization in general, and algorithmic game theory in particular. A large part of my research is concerned with the development of efficient algorithms for optimization problems. Another part is about devising algorithmic means to reduce the inefficiency caused by selfish behavior in large distributed systems. My research is fundamental in nature, but addresses several real-world aspects that are of practical relevance (such as lack of coordination, uncertainty of data, limitations of resources). Results of this research find their applications, for instance, in logistics, transportation, traffic and network routing, scheduling and auctions.

I am the group leader of the Networks and Optimization group at CWI.

Prof. Leen Stougie

Full Professor

My research interests are mathematics on the interface of operations research, combinatorial optimization and probability theory, with applications in life sciences and logistics. My interest in these fields is focused on the design and analysis of algorithms. A frequent motif in my research is optimization under uncertainty : problems where the input is uncertain in structured ways, or is learnt over time. My recent work in bioinformatics focuses on metabolic network analysis and algorithms for phylogenetic networks, and I am a member of the European Research Team ERABLE.

I have authored over 100 papers in journals, reviewed conference proceedings and books. I now lead the Life Sciences and Health group at CWI.

Prof. Gerrit Timmer

Full Professor

I am one of the founders, and current Chief Financial Officer, of ORTEC, a world leader in optimization software and analytics solutions.

I teach the Integrative practical in the bachelor's programme, and the OR Case course in the master's programme. Both of these courses are about learning how to transfer the theoretical knowledge learned in the programme into solutions for real problems.

Dr. Ad Ridder

Associate Professor

My main research interest is in the field of stochastic computer simulation, and specifically for estimating rare-event probabilities. For instance, one can think of the probability that the reserve of an insurance product is ruined because of many large claims. Or the probability that an electricity network is blacked out due to high demand or low power generating. The purpose of this research is developing efficient simulation algorithms, and proving that these algorithms are actually efficient.

In 2000, I received the Goodeve Medal for the most outstanding contribution to a journal published by the Operational Research Society (UK); this paper was about a queueing model of the prison system in the Netherlands! I recently received the Best Theoretical Paper Award at the 2016 Winter Simulation Conference.

I am also interested in probabilistic counting using simulation. For instance, consider the self-avoiding walk in the plane: you start in the origin and choose at random one of the four cardinal directions, North, East, South, West. Then you take a step of length one in the chosen direction. From then on you do the same, choose randomly a direction and taking a step. You do this until you come back to a point that you have previously visited. The walk until that point is self-avoiding, and it resembles a linear polymer or a protein string in two dimensions (in reality, three dimensions). The question is: how many different self-avoiding walks are there of at least a certain length? Say at least 100 steps, or 200, or even larger. The exact number is unknown but can be approximated by smart simulation algorithms.

I obtained my PhD in Operations Research from Leiden University in 1987, after which I worked for two years at a software company before returning to academia. I teach courses on stochastic operations research, numerical methods, and computer simulation. My work is published in academic journals, presented at international scientific conferences, and appeared in a book Fast Sequential Monte Carlo Methods for Counting and Optimization, published by Wiley in 2014.

Recently I organized two international workshops that were held in Amsterdam: the 10th International Workshop on Rare Event Simulation in 2014, and the 11th Workshop on Retrial Queues and Related Topics in 2016. Since 2011 I have served as a board member of the Netherlands Society of Statistics and Operations Research.

Dr. René Sitters

Associate Professor

I work in the area of combinatorial optimization. One focus of my work is on variants of the famous travelling salesman problem, which asks for the shortest tour through a given collection of locations. Another focus is on scheduling problems — allocating tasks to resources under various kinds of constraints, with the goal of having the tasks executed as quickly as possible. A third is online problems, where information about the problem is learnt while the problem is being solved.

Dr. Neil Olver

Assistant Professor

My research focuses on the design of efficient algorithms that give good solutions to computationally hard optimization problems. For example, problems that arise in the design of computer or transportation networks. I am also interested in the intersection of probability and game theory with optimization — for example, in understanding the behaviour of traffic.

I am also affiliated with CWI (the Dutch Institute for Mathematics and Computer Science). I have been awarded both a VENI and TOP grant by the Dutch Organization for Scientific Research. I was previously an Applied Mathematics Instructor at MIT, and completed my PhD at McGill University. My use of technology in teaching is highlighted in a video by the SBE Innovations Center.

One example of a problem I have worked on extensively is the Steiner tree problem. It's very simple to state: we are given some kind of network, and some collection of terminals within this network, and our goal is to "buy" some edges in the network so that all of these terminals are connected. The goal is to find the cheapest possible collection of edges to buy. While this may sound simple, it is in fact one of the primary examples of an NP-hard problem — this means that we don't believe that it's possible to always solve these problems in a computationally efficient manner. It is intrinsically a difficult problem when the instance is large.

Steiner tree example

Here we see a very small network (the black edges), with five terminals (the black squares) that need to be connected. The blue lines indicate one possible collection of edges that connect all the terminals (but it is not the cheapest possible solution).

While applied problems are typically much more complicated, the fundamental aspect of connectivity appears often, and so obtaining a deep understanding of the Steiner tree problem is very important. My work focuses on approximation algorithms — algorithms that are fast, and return a solution that is guaranteed to not be much more expensive than the optimum. For the Steiner tree problem, the best-known such algorithms return a solution that is at most 1.39 times the cheapest possible. Doing better is a major open problem.

Dr. Dinard van der Laan

Assistant Professor

In my research I consider optimization problems for which it is hard to obtain an optimal solution. The model description of such problems usually contains a large number of states for which a solution (often called a policy or strategy) specifies for each state which action (possibly randomized) should be performed.

For such optimization problems I am especially interested in algorithms which iteratively find better solutions. Another goal of my research is to obtain structural optimality results. Such a result can be for example that an optimal (or close to optimal) solution can be found within a specific subset of all solutions. Such a result is useful if optimization over that subset can be done more efficiently than over all solutions. The problems originate from diverse areas as the control of queueing systems, auctions and dynamic pricing.

I pursued my PhD in the mathematics department of Leiden University, followed by a postdoc at INRIA Grenoble-Rhone-Alpes in France, before joining the VU.