Speakers

Martine Labbé, Université Libre de Bruxelles [ORCID]

Short bio: Martine Labbé is an honorary professor at the Université Libre de Bruxelles. She was a full professor from 2000 to 2019 and was president of EURO between 2007 and 2008. She has contributed substantially to various topics, including bilevel optimization, network optimization, location problems, routing, and machine learning. In 2019, she was the first female researcher to receive the EURO Gold Medal, the highest distinction in our field in Europe.

Title: TBD
Abstract: TBD

Stefan Røpke, Technical University of Denmark [ORCID]

Short bio: Stefan Røpke has been Professor of Operations Research at the Technical University of Denmark since 2012. His research has provided numerous contributions on exact and heuristic methods to solve routing and logistics problems, including the well-known Adaptive Large Neighborhood Search meta-heuristic, co-authored with David Pisinger.

Title: TBD
Abstract: TBD

Wolfram Wiesemann, Imperial College Business School [ORCID]

Short bio: Wolfram Wiesemann is Professor of Analytics & Operations at the Analytics, Marketing & Operations department at Imperial College Business School. His research interests evolve around decision-making under uncertainty, with applications to logistics, supply chain management and healthcare. Wolfram has served as an elected member of the boards of the Mathematical Optimization Society and the Stochastic Programming Society, and he currently serves as editor in-chief of Operations Research Letters as well as a department co-editor for Management Science.

Title: Large-Scale and Data-Driven Markov Decision Processes
Abstract: Markov decision processes (MDPs) constitute one of the predominant modeling and solution paradigms for dynamic decision problems affected by uncertainty. MDPs model the dynamics of a system through a random state evolution that generates rewards over time. The decision maker aims to select actions that influence this state evolution so as to maximize rewards. In this talk, we review recent advances in MDPs along two directions: (i) the construction of data-driven policies that combine the (traditionally separated) tasks of estimating the system’s behavior and selecting actions that maximize rewards in the estimated system, and (ii) the exploitation of structure to solve large-scale problems. In view of (i), we will show how the consideration of data-driven policies naturally leads to the study of robust MDPs, where the decision maker combats overfitting by hedging against the worst system dynamics that are plausible under some given training data. We will also discuss how alternative models of robustness offer different trade-offs between the competing goals of out-of-sample performance and complexity of the involved policies and computations. As for (ii), we will review two types of structure that allow us to alleviate the well-known curse of dimensionality: weakly coupled MDPs that combine a potentially large number of MDPs via a small number of linking constraints, and factored MDPs whose states are represented by assignments of values to state variables that evolve and contribute to the system’s rewards largely independently.

Hande Yaman Paternotte, KU Leuven [ORCID]

Short bio: Hande Yaman Paternotte is Professor of Operations Research at the Faculty of Economics and Business at KU Leuven. Research contributions and interests of her include polyhedral approaches for mixed integer programming with applications in production planning, logistics, and network design.

Title: Partitioning a Graph into Connected Components
Abstract: In this talk, we study problems that involve partitioning the vertices of an undirected graph into a given number of pairwise disjoint sets such that each set induces a connected subgraph. We first propose valid inequalities, which extend and generalize the ones in the literature, and report on computational experiments demonstrating their use (joint work with P. Moura and R. Leus). Then, we extend this problem to also compute a spanning tree for each set of the partition such that the weight of the heaviest tree is minimized. We investigate the complexity of this problem and present formulations and solution methods, which we compare with an experimental study (joint work with M. Davari and P. Moura). Finally, we consider a practical problem encountered in power system restoration, which involves partitioning a power network into connected subnetworks, one for each black start generator, such that the restoration time is minimized. We propose a solution method that uses a new formulation and properties of optimal solutions and report computational results (joint work with H. Çalık and D. Van Hertem).

Special Roundtable Speakers

As the next edition of the AIROYoung Workshop is a milestone anniversary, we invite the founders of AIROYoung and the members of the previous boards to join (on-site or remotely) the special roundtable about the past, present, and future of the association. Speakers include:

• Lavinia Amorosi, Sapienza University of Rome [ORCID]
• Michele Barbato, University of Milan [ORCID]
• Veronica Dal Sasso, Siemens Mobility, former University of Padova [ORCID]
• Martina Fischetti, University of Sevilla [ORCID]
• Serena Fugaro, Italian National Research Council [ORCID]
• Giusy Macrina, University of Calabria [ORCID]
• Valentina Morandi, University of Brescia [ORCID]
• Lorenzo Peirano, University of Brescia [ORCID]
• Alberto Santini, Universitat Pompeu Fabra [ORCID]