J. A. MESA LOPEZ COLMENAR, A. Schöbel
Robust Optimization is the branch of Mathematical Optimization that deals with problems where the parameters or input are uncertain, and it is not known or is not applicable to this uncertainty a known probability distribution. The uncertainty can have many varied reasons, two of which are unknown data (due to measurement errors or some behavior of customers that only can be estimated) or disturbances/perturbations produced by environmental to system effects. In this paper, we suppose that the coordinates of the demand points are uncertain, but they belong to a given neighborhood of the nominal scenario. We apply several approaches of robust optimization to a broad class of planar location problems, some of them are strict, cardinality-constrained, Ben-Tal and Nemirowski reliability, and light robustness. In this work, we present explicit examples and compare their solutions. Moreover, we present an application for transportation planning.
Palabras clave: Continuous location, robust optimization
Programado
Localización (GELOCA4)
13 de junio de 2025 11:00
Sala 3. Maria Rúbies Garrofé