Exact Approaches for Convex Adjustable Robust Optimization

Jointly with Enrico Malaguti, Michele Monaci

Year: 2023

Abstract: Adjustable Robust Optimization (ARO) is a paradigm for facing uncertainty in a decision problem, in case some recourse actions are allowed after the actual value of all input parameters is revealed. While several approaches have been introduced for the linear case, little is known regarding exact methods for the convex case. In this work, we introduce a new general framework for attacking ARO problems involving convex functions in the recourse problem. We first recall a semi-infinite reformulation of the problem and, provided that one can solve a non-convex separation problem, show how to solve it either by a generalized Benders decomposition or by a column-and-constraint generation approach. For the relevant case in which the uncertainty set has an affine mapping to a 0-1 polytope, we show that the separation problem can be reformulated as a convex Mixed-Integer NonLinear Problem, thus allowing us to derive computationally sound exact methods. Finally, we apply the resulting algorithms to two different applications, namely a nonlinear facility location problem and a nonlinear resource allocation problem, to numerically assess their computational performance.

Cite as:

@techreport{lefebvre2022exact,
  title={Exact Approaches for Convex Adjustable Robust Optimization},
  author={Henri Lefebvre and Enrico Malaguti and Michele Monaci},
  year={2022},
  note={Updated: 2025/04/18},
  archivePrefix={Optimization Online},
  url={https://optimization-online.org/?p=20869}
}

Open Access

Optimization Online

Open Data

All the data which were used are available on our GitHub. FLP instances RAP instances

Open Methodology

Our R reports are available.
FLP: Compiled version
RAP: Compiled version

Open Source

Our code is publicly available on the GitHub repository hlefebvr/AD-convex-adjustable-robust-optimization

Open Educational Resources