Martine Labbé. Université Libre de Bruxelles
Martine Labbé is honorary professor at the Université Libre de Bruxelles (ULB). From 1999 to 2019 she was Professor of Operations Research at the Computer Science Department of the Faculty of Sciences. Her main research area is discrete optimization, including graph theory and integer programming problems and with a particular emphasis on location and network design problems. She is also specialised in bilevel programming and studies pricing problems and Stackelberg games. In 2007-2008, she was president of EURO, the Association of European Operational Research Societies. She was, in 2014 and 2015, Vice-Chair of the SIAM Activity Group on Optimization (SIAG/OPT). In June 2019 she was awarded the EURO Gold Medal.
Linear bilevel optimization: overview and recent results
A bilevel optimization problem consists in an optimization problem in which some of the constraints specify that a subset of variables must be an optimal solution to another optimization problem. This paradigm is particularly appropriate to model competition between agents, a leader and a follower, acting sequentially.
In this talk I will focus on the simplest bilevel problems, those that are linear. I will present the main characteristics, properties and algorithms for these problems. Then, I will discuss some recent results showing that these problems are already extremely challenging.
Mihalis Yannakakis. Columbia University
Professor Mihalis Yannakakis studied at the National Technical University of Athens (Diploma in Electrical Engineering, 1975), and at Princeton University (PhD in Computer Science, 1979). He worked at Bell Labs Research from 1978 until 2001, as Member of Technical Staff (1978-1991) and as Head of the Computing Principles Research Department (1991-2001). He was Director of Computing Principles Research at Avaya Labs (2001-2002), and Professor of Computer Science at Stanford University (2002-2003), and he joined Columbia University in 2004. His research interests include computational complexity, databases, and other related fields, as design and analysis of algorithms, combinatorial optimization, game theory, and modelling, verification and testing of reactive systems. He won the Donald E. Knuth Prize in 2005.
Approximation of Multiobjective Optimization Problems
When evaluating different solutions from a design space, it is often the case that more than one criteria come into play. The trade-off between the different criteria is captured by the so-called Pareto curve. The Pareto curve has typically an exponential number of points. However, it turns out that, under general conditions, there is a polynomially succinct curve that approximates the Pareto curve within any desired accuracy.
This talk will discuss problems concerning the efficient computation of good approximate solutions for multiobjective problems. In the first part of the talk we address the question of when an approximate Pareto curve can be computed in polynomial time. We discuss general conditions under which this is the case, and relate the multiobjective approximation to the single objective case. In the second part of the talk, we address the problem of computing efficiently a good approximation of the trade-off curve using as few solutions (points) as possible. If we are to select only a limited number of solutions, how shall we pick them so that they represent as accurately as possible the spectrum of possibilities?
Laureano F. Escudero, Universidad Rey Juan carlos (Madrid, Spain)
Laureano F. Escudero, full professor retired and currently Research Fellow, Universidad Rey Juan Carlos (Spain), specialized on Stochastic Optimization, has been President of EURO, 2003-2004, supervised 12 PhD theses, author of 5 books and co-author of another one, and author/co-author of 150+ on OR, particularly, Mathematical Optimization, is a holder of an OR medal awarded by SEIO (the Spanish Statistics and Operations Research Society). He has worked for IBM Research, Scientific and Development Centers in Madrid (Spain). Palo Alto (California), Sindelfingen (Germany), and T.J. Watson Research Center (YH, NY).
On dynamic multiple allocation capacitated hub location expansion planning under uncertainty
Joint work with Juan F. Monge, Center of Operations Research, UMH (Universidad Miguel Hernandez de Elche), Alicante, Spain, firstname.lastname@example.org.
This work focuses on a stochastic mixed integer linear optimization modeling framework and a matheuristic approach for solving the multistage capacitated allocation hub location network expansion planning under uncertainty. The strategic decisions are the hub location in a network and their initial capacity dimensioning as well as its expansion along a time horizon. Two types of uncertain parameters are considered, namely, strategic and operational ones. The strategic uncertainty is stagewise-dependent. The operational uncertainty is stage-dependent, both being captured by a finite set of scenarios. Given the dimensions of the instances in rea-life applications (due to the large-scale hub network dimensions and the cardinality of the joint strategic multistage operational two-stage scenario trees to properly represent the inherent uncertainty, it is unrealistic to seek for the optimal solution. So, a sort of matheuristics should be looked for. The so-named SFR3 matheuristic decomposition algorithm is introduced for Scenario variables Fixing and constraints and binary variables' integrality iteratively Randomized Relaxation Reduction, where several strategies are considered. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data.
Keywords: hub network location, stochastic optimization, multistage network expansion planning, strategic and tactical uncertainties, fix-and-randomized-relaxation-reduction matheuristic.