 Robust recoverable 0–1 optimization problems under polyhedral uncertaintyMikita Hradovich, Adam Kasperski and Paweł Zieliński European Journal of Operational Research, 2019, vol. 278, issue 1, 136-148 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. Keywords: Robustness and sensitivity analysis; Combinatorial optimization; Recoverable robustness; Polyhedral uncertainty (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:278:y:2019:i:1:p:136-148 DOI: 10.1016/j.ejor.2019.04.017 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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