Making a case for robust optimization models

Dawei Bai, Tamra Carpenter, John Mulvey

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

Robust optimization searches for recommendations that are relatively immune to anticipated uncertainty in the problem parameters. Stochasticities are addressed via a set of discrete scenarios. This paper presents applications in which the traditional stochastic linear program fails to identify a robust solution - despite the presence of a cheap robust point. Limitations of piecewise linearization are discussed. We argue that a concave utility function should be incorporated in a model whenever the decision maker is risk averse. Examples are taken from telecommunications and financial planning.

Original languageEnglish (US)
Pages (from-to)895-907
Number of pages13
JournalManagement Science
Volume43
Issue number7
DOIs
StatePublished - Jul 1997

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research

Keywords

  • Decomposition Algorithm
  • Financial Planning
  • Nonlinear Objective
  • Robust Optimization
  • Telecommunication Network
  • Utility Function

Fingerprint

Dive into the research topics of 'Making a case for robust optimization models'. Together they form a unique fingerprint.

Cite this