Robust Optimization Made Easy with ROME

Joel Goh () and Melvyn Sim ()
Additional contact information
Joel Goh: Stanford Graduate School of Business; NUS Business School, National University of Singapore, Singapore 119245, Republic of Singapore
Melvyn Sim: NUS Business School and NUS Risk Management Institute, National University of Singapore, Singapore 119245, Republic of Singapore

Operations Research, 2011, vol. 59, issue 4, 973-985

Abstract: We introduce ROME, an algebraic modeling toolbox for a class of robust optimization problems. ROME serves as an intermediate layer between the modeler and optimization solver engines, allowing modelers to express robust optimization problems in a mathematically meaningful way. In this paper, we discuss how ROME can be used to model (1) a service-constrained robust inventory management problem, (2) a project-crashing problem, and (3) a robust portfolio optimization problem. Through these modeling examples, we highlight the key features of ROME that allow it to expedite the modeling and subsequent numerical analysis of robust optimization problems. ROME is freely distributed for academic use at http://www.robustopt.com.

Keywords: robust optimization; algebraic modeling toolbox; MATLAB; stochastic programming; decision rules; inventory control; PERT; project management; portfolio optimization (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.1110.0944 (application/pdf)

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:inm:oropre:v:59:y:2011:i:4:p:973-985

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().