Solving mixed-integer robust optimization problems with interval uncertainty using Benders decomposition

Sauleh Siddiqui (), Steven A Gabriel and Shapour Azarm
Additional contact information
Steven A Gabriel: Department of Civil Engineering, Applied Mathematics & Statistics, and Scientific Computation Program, University of Maryland, College Park, MD, USA
Shapour Azarm: Department of Mechanical Engineering, Applied Mathematics & Statistics, and Scientific Computation Program, University of Maryland, College Park, MD, USA

Journal of the Operational Research Society, 2015, vol. 66, issue 4, 664-673

Abstract: Uncertainty and integer variables often exist together in economics and engineering design problems. The goal of robust optimization problems is to find an optimal solution that has acceptable sensitivity with respect to uncertain factors. Including integer variables with or without uncertainty can lead to formulations that are computationally expensive to solve. Previous approaches for robust optimization problems under interval uncertainty involve nested optimization or are not applicable to mixed-integer problems where the objective or constraint functions are neither quadratic, nor linear. The overall objective in this paper is to present an efficient robust optimization method that does not contain nested optimization and is applicable to mixed-integer problems with quasiconvex constraints (⩽ type) and convex objective funtion. The proposed method is applied to a variety of numerical examples to test its applicability and numerical evidence is provided for convergence in general as well as some theoretical results for problems with linear constraints.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v66/n4/pdf/jors201441a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v66/n4/full/jors201441a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:66:y:2015:i:4:p:664-673

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().