Robust optimization with belief functions
Data Structures and Algorithms
2023-03-10 v1 Optimization and Control
Abstract
In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing possible realizations of the objective function coefficients. The concept of belief function in the traditional and possibilistic setting is applied to define a set of admissible probability distributions over the scenario set. The generalized Hurwicz criterion is then used to compute a solution. In this paper, the complexity of the resulting problem is explored. Some exact and approximation methods of solving it are proposed.
Cite
@article{arxiv.2303.05067,
title = {Robust optimization with belief functions},
author = {Marc Goerigk and Romain Guillaume and Adam Kasperski and Paweł Zieliński},
journal= {arXiv preprint arXiv:2303.05067},
year = {2023}
}