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A Framework for Selecting a Selection Procedure

A Framework for Selecting a Selection Procedure A Framework for Selecting a Selection Procedure ROLF WAEBER, PETER I. FRAZIER, and SHANE G. HENDERSON, Cornell University 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. Categories and Subject Descriptors: I.6.1 [Simulation and Modeling]: Simulation Theory; G.4 [Mathematics of Computing]: Mathematical Software--Algorithm design and analysis General Terms: Performance, Algorithms, Theory Additional Key http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Modeling and Computer Simulation (TOMACS) Association for Computing Machinery

A Framework for Selecting a Selection Procedure

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1049-3301
DOI
10.1145/2331140.2331144
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Abstract

A Framework for Selecting a Selection Procedure ROLF WAEBER, PETER I. FRAZIER, and SHANE G. HENDERSON, Cornell University 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. Categories and Subject Descriptors: I.6.1 [Simulation and Modeling]: Simulation Theory; G.4 [Mathematics of Computing]: Mathematical Software--Algorithm design and analysis General Terms: Performance, Algorithms, Theory Additional Key

Journal

ACM Transactions on Modeling and Computer Simulation (TOMACS) – Association for Computing Machinery

Published: Aug 1, 2012

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