.png){kind=icon}
Return
Variable-sized uncertainty and inverse problems in robust optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2017.06.042.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In robust optimization, the general aim is to find a solution that performs well over a set of possible parameter outcomes, the so-called uncertainty set. In this paper, we assume that the uncertainty size is not fixed, and instead aim at finding a set of robust solutions that covers all possible uncertainty set outcomes. We refer to these problems as robust optimization with variable-sized uncertainty. We discuss how to construct smallest possible sets of min-max robust solutions and give bounds on their size. A special case of this perspective is to analyze for which uncertainty sets a nominal solution ceases to be a robust solution, which amounts to an inverse robust optimization problem. We consider this problem with a min-max regret objective and present mixed-integer linear programming formulations that can be applied to construct suitable uncertainty sets. Results on both variable-sized uncertainty and inverse problems are further supported with experimental data. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Robustness and sensitivity analysis
Uncertainty sets
Inverse optimization
Optimization under uncertainty
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
Cited Papers
Effects of Group Counseling Transmitted Through Videoconferencing on Changes in Eating Behaviors
.png){kind=icon}
PREAI
ShareSaveShareSaveNumerical analysis of heat transfer on a rotating disk surface under confined liquid jet impingement
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more