A Framework for Selecting a Selection Procedure

Abstract

For many discrete simulation optimization applications, it is often difficult to decide which Ranking and Selection (R&S) procedure to use. To efficiently compare R&S procedures, we present a three-layer performance evaluation process. We show that the two most popular performance formulations, namely the Bayesian formulation and the indifference zone formulation, have a common representation analogous to convex risk measures used in mathematical finance. We then specify how a decision maker can impose a performance requirement on R&S procedures that is more adequate for her risk attitude than the indifference zone or the Bayesian performance requirements. Such a performance requirement partitions the space of R&S procedures into acceptable and nonacceptable procedures. The minimal computational budget required for a procedure to become acceptable introduces an easy-to-interpret preference order on the set of R&S policies. We demonstrate with a numerical example how the introduced framework can be used to guide the choice of selection procedure in practice.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 01, 2012
Source ID
10.1145/2331140.2331144

Entities

People

  • Peter Frazier
  • Rolf Waeber
  • Shane Henderson

Organizations

  • Air Force Office of Scientific Research
  • Cornell University
  • Division of Civil, Mechanical & Manufacturing Innovation

Tags

Fields of Study

  • Computer science

Readers

  • Naval Architecture and Marine Engineering.
  • Operations Research
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space