A practical approach to subset selection for multi-objective optimization via simulation

Currie, Christine and Monks, Thomas (2021) A practical approach to subset selection for multi-objective optimization via simulation. ACM Transactions on Modeling and Computer Simulation, 31 (4), 1-15, [20]. (doi:10.1145/3462187).

Record type: Article

Abstract

We describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well and we also demonstrate its use
analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital.

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Accepted/In Press date: 5 March 2021

e-pub ahead of print date: 16 August 2021

Published date: 1 October 2021

Additional Information: Funding Information: This report is independent research funded by the National Institute for Health Research Applied Research Collaboration Wessex. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. Authors’ addresses: C. S. M. Currie, University of Southampton, Highfield, Southampton, SO17 1BJ, UK; email: christine. currie@soton.ac.uk; T. Monks, University of Exeter, St Luke’s Campus, Heavitree Road, Exeter, EX1 2LU, UK; email: t.m.w.monks@exeter.ac.uk. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2021 Copyright held by the owner/author(s). 1049-3301/2021/08-ART20 $15.00 https://doi.org/10.1145/3462187 Publisher Copyright: © 2021 Owner/Author.

Keywords: Ranking and selection, simulation, subset selection, chance constraints