Robust recoverable 0-1 optimization problems under polyhedral uncertainty
Abstract
This paper deals with a robust recoverable approach to 0-1 programming problems. It is assumed that a solution constructed in the first stage can be modified to some extent in the second stage. This modification consists in choosing a solution in some prescribed neighborhood of the current solution. The second stage solution cost can be uncertain and a polyhedral structure of uncertainty is used. The resulting robust recoverable problem is a min-max-min problem, which can be hard to solve when the number of variables is large. In this paper we provide a framework for solving robust recoverable 0-1 programming problems with a specified polyhedral uncertainty and propose several lower bounds and approximate solutions, which can be used for a wide class of 0-1 optimization problems. The results of computational tests for two problems, namely the assignment and the knapsack ones, are also presented.
Cite
@article{arxiv.1811.06719,
title = {Robust recoverable 0-1 optimization problems under polyhedral uncertainty},
author = {Mmikita Hradovich and Adam Kasperski and Pawel Zielinski},
journal= {arXiv preprint arXiv:1811.06719},
year = {2018}
}