Abstract and Bio Speakers NGB/LNMB Seminar

Back to school,
learn about the latest developments in Operations Research

Prof.dr.ir Dick den Hertog (Tilburg University)

Brief resume: Dick den Hertog is a professor of Operations Research at Tilburg University, the Netherlands. His research interests cover various fields in linear and nonlinear optimization, e.g. robust optimization and simulation-based optimization. He is also active in applying optimization theory in real-life applications. He is directory of Tilburg Center for Optimization (Ticopt).

Title: Practical robust optimization - part 1 and 2

Abstract: This tutorial will provide you a basic understanding of practical robust optimization, which is indispensable for each Operations Research practitioner.
Optimization problems in practice often contain parameters that are uncertain, due to e.g. estimation or rounding. The idea of robust optimization is to find a solution that is immune against these uncertainties. The last decade efficient methods have been developed to find such robust solutions. The underlying idea is to formulate an uncertainty region for the uncertain parameters, and then require that the constraints should hold for all parameter values in this uncertainty region. It can be shown that e.g. for linear programming, for the most important choices of the uncertainty region, the final problem can be reformulated as linear programming or conic quadratic programming problems, for which very efficient solvers are available nowadays. In this talk we restrict ourselves to linear programming. We will treat the basics of robust linear optimization, and also show the huge value of robust optimization in (dynamic) multistage problems. Robust optimization has already shown its high practical value in many fields: logistics, engineering, finance, medicine, etc. Some state-of-the-art modelling packages have already implemented robust optimization technology.

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Prof.dr. Jeff Linderoth (University of Wisconsin-Madison)

Brief resume: Jeff Linderoth is a Professor in the departments of Industrial and Systems Engineering and Computer Sciences (by courtesy) at the University of Wisconsin-Madison, joining both departments in 2007.
Dr. Linderoth received his Ph.D. degree from the Georgia Institute of Technology in 1998. From 1998-2000, he was employed with the Mathematics and Computer Science Division at Argonne National Laboratory, and from 2000-2002, he was a Senior Consultant with the optimization-based financial products firm of Axioma Inc. Prior to joining University of Wisconsin-Madison, from 2002-2007, he was a Assistant Professor at Lehigh University. His research interests lie in the areas of discrete optimization, stochastic programming, and computing. In 2002, he was awarded with his coauthors the SIAM Activity Group on Optimization Prize. He currently is an area editor for Mathematical Programming Computation and serves on the editorial boards of four additional journals, including the INFORMS Journal on Computing and Operations Research. Dr. Linderoth has been involved in the development of the mixed integer nonlinear programming software packages FilMINT and MINOTAUR..

Title: Mixed Integer Nonlinear Programming: Background

Abstract: The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Discrete components model phenomena such as fixed charges, dichotomies, piecewise linear functions, and general logical relationships between system entities. Nonlinearities are required to accurately represent phenomena such as covariance, economies of scale, and queuing delays, or physical properties such as pressure, stress, and equilibrium. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems.
During the talk the theoretical foundations behind modern solution approaches to MINLP will be described. Discussion will center around building blocks such as relaxations, cutting planes, and search techniques that are all combined to build successful algorithms.

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Marcel Hunting (Paragon)

Brief resume: Marcel Hunting is a software developer in the Algorithmic Capabilities group at Paragon Decision Technology, developers of the modeling system AIMMS. He received his Ph.D. in 1998 at the University of Twente. His main research interest is discrete optimization. Marcel is the main developer of the AIMMS Outer Approximation (AOA) solver for handling mixed integer nonlinear programming problems.

Title: Practical application of MINLP

Abstract: We show how recent developments in mixed integer programming, both linear and nonlinear, have been used in improving software for solving mixed integer nonlinear programming (MINLP) problems. Examples of those developments are preprocessing, probing, callbacks and new heuristics. We demonstrate the benefits by using some real-life problems.

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Ruud Brekelmans (Tilburg University)

Brief resume: Ruud Brekelmans is assistant professor at the Department of Econometrics & Operations Research of Tilburg University, where he received his Ph.D. in 2000. He is directory of Tilburg Center for Optimization (Ticopt). His research interests are inventory management and optimization and he is especially interested in applied research.

Title: Mixed Integer Nonlinear Programming Applied To Dike Height Optimization

Abstract: Water-related disasters are an important proportion of natural disasters all over the earth, and due to global warming this proportion is likely to increase even more in the future. Many of these water-related disasters are caused by flooding. Almost half a billion people live on or near deltas, often in megacities, and many of those have experienced severe flooding in the past decade. Floods can have an enormous impact on a region caused by loss of human lives and economical damage. Because the benefit of investing in risk-reducing measures is not immediately visible, and because the probability of such a disaster is often very small, many governments do not pay as much attention to protection as they probably should. Since the severe flooding disaster in the south-western parts of the Netherlands in 1953, the Dutch government is very much aware of the flooding risks that The Netherlands is exposed to. Soon after 1953, Van Dantzig started the cost-benefit analysis of the dike height optimization problem, which tries to find the optimal balance between investing in dikes (costs) and reducing the risks of floods (benefits). Both increasing flood probabilities and economical growth prevent a static solution and enforce repeated future investments in dikes. We extend Van Dantzig's model and try to to answer the fundamental questions of when and how much to invest in which parts of the constituent segments of a dike ring protecting a certain area of land. This problem is solved by formulating it as a Mixed-Integer Nonlinear Programming (MINLP) model. A solution method is proposed that combines the use of state-of-the-art solvers with techniques that exploit the problem structure. Dutch government agencies use the model to analyze the main dike rings in The Netherlands and to propose new safety standards to be incorporated in the Dutch Water Act.