Solution of Robust Linear Optimization Problems

Fuente: arXiv
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Hauptverfasser: Mondal, Parthasarathi, Ojha, Akshay Kumar
Format: Preprint
Veröffentlicht: 2025
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author Mondal, Parthasarathi
Ojha, Akshay Kumar
author_facet Mondal, Parthasarathi
Ojha, Akshay Kumar
contents Robust optimization(RO) is an important tool for handling optimization problem with uncertainty. The main objective of RO is to solve optimization problems due to uncertainty associated with constraints satisfying all realizations of uncertain values within a given uncertainty set. The challenge of RO is to reformulate the constraints so that the uncertain optimization problem is transformed into a tractable deterministic form. In this paper, we have given more emphasis to study the robust counterpart(RC) of the RO problems and have developed a mathematical model on the solution strategy for robust linear optimization problems, where the constraints only are associated with uncertainties. The box and ellipsoidal uncertainty sets are considered and some illustrative numerical examples have been solved in each corresponding case for validating our proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2504_00894
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Solution of Robust Linear Optimization Problems
Mondal, Parthasarathi
Ojha, Akshay Kumar
Optimization and Control
Robust optimization(RO) is an important tool for handling optimization problem with uncertainty. The main objective of RO is to solve optimization problems due to uncertainty associated with constraints satisfying all realizations of uncertain values within a given uncertainty set. The challenge of RO is to reformulate the constraints so that the uncertain optimization problem is transformed into a tractable deterministic form. In this paper, we have given more emphasis to study the robust counterpart(RC) of the RO problems and have developed a mathematical model on the solution strategy for robust linear optimization problems, where the constraints only are associated with uncertainties. The box and ellipsoidal uncertainty sets are considered and some illustrative numerical examples have been solved in each corresponding case for validating our proposed method.
title Solution of Robust Linear Optimization Problems
topic Optimization and Control
url https://arxiv.org/abs/2504.00894