Ranking and selection meets robust optimization

Ilya O. Ryzhov, Boris Defourny, Warren B. Powell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


The objective of ranking and selection is to efficiently allocate an information budget among a set of design alternatives with unknown values in order to maximize the decision-maker's chances of discovering the best alternative. The field of robust optimization, however, considers risk-averse decision makers who may accept a suboptimal alternative in order to minimize the risk of a worst-case outcome. We bring these two fields together by defining a Bayesian ranking and selection problem with a robust implementation decision. We propose a new simulation allocation procedure that is risk-neutral with respect to simulation outcomes, but risk-averse with respect to the implementation decision. We discuss the properties of the procedure and present numerical examples illustrating the difference between the risk-averse problem and the more typical risk-neutral problem from the literature.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 Winter Simulation Conference, WSC 2012
StatePublished - 2012
Event2012 Winter Simulation Conference, WSC 2012 - Berlin, Germany
Duration: Dec 9 2012Dec 12 2012

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2012 Winter Simulation Conference, WSC 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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