Optimisation under uncertainty for real-world systems: theoretical aspects and practical challenges.
Fabricio Oliveira
Abstract
In this talk, we introduce the framework of optimisation under uncertainty, which consists of a collection of disciplines such as stochastic programming, robust optimisation, scenario generation, decomposition methods, and others related. When properly combined, these allow the development of mathematical programming-based decision support tools that can meaningfully consider the inherent uncertainty associated with input data. We illustrate the capabilities of such a framework through examples derived from real-world problems in which the combination of two or more of these disciplines allowed the development of enhanced models which, ultimately, led to more efficient decision support tools. We will also discuss some of the technical details behind the development of these applications and present future perspectives in terms of research development.
Time: Fri 2023-11-24 11.00 - 12.00
Location: Seminar room 3721
Video link: Zoom ID 63658381373
Language: English
Participating: Fabricio Oliveira
Speaker
Fabricio Oliveira
Associate Professor
Systems Analysis Laboratory
Department of Mathematics and Systems Analysis
Aalto University, School of Science
Fabricio Oliveira is an Associate Professor of Operations Research in the School of Science at Aalto University (Finland). He holds a B.Sc. (2008) and a D.Sc (2012) in Production Engineering from PUC-Rio (Brazil). Previous to his current appointment, he has worked as a visiting researcher at the Centre of Advanced Process Decision-making (CAPD) at Carnegie Mellon University (USA), as a Postdoctoral Research Fellow in the Mathematical Sciences Department at RMIT University (Australia), and as an Assistant Professor in the Industrial Engineering Department at PUC-Rio. His main research interests are practical and computational challenges of applying optimisation under uncertainty for solving production planning and supply chain management problems.