The Speakers

Two-stage robust optimization problems with integer recourse are a notoriously difficult class of problems, yet they model many important applications. In this talk, we discuss heuristic approaches for solving these problems by addressing the first-stage problem through local search algorithms. We focus on the case where all decision variables, as well as the uncertainty set, are discrete. We numerically compare the performance of our algorithms with that of a recently proposed exact algorithm from the literature.

Many problems in quantum computing have inherently uncertain data. For example, the energies that specify a system are only known approximately. Also, current hardware is noisy and its error rates might change between the time it is calibrated and the time an algorithm actually runs. After an introduction to quantum basics and notation, I will describe some problems where uncertainty arises naturally and where the tools of robust optimization seem to fit. The aim of this talk is less to present finished results than to point to problems and questions that might be interesting for future research.

Igor Malheiros received his PhD in Computer Science from Université de Montpellier, France, in 2026, under the supervision of Michael Poss and Anand Subramanian, with a research focus on robust optimization for routing problems. During his PhD, conducted in collaboration with Atoptima in Bordeaux, France, he developed scalable optimization algorithms for supply chain planning. His research interests include integer programming, robust optimization, exact and heuristic methods, and optimization applications in routing, logistics, and supply chain.

Christian Biefel focused on theoretical aspects of robust optimization during his PhD at FAU Erlangen-Nürnberg. His research centered on complexity and approximation bounds for robust variants of optimization problems, e.g. complementarity problems and network flows under arc failures. Since 2022 he works at SAP, where he develops quantum algorithms for supply chain optimization.