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

A practical approach to subset selection for multi-objective optimization via simulation
A practical approach to subset selection for multi-objective optimization via simulation
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.
Ranking and selection, simulation, subset selection, chance constraints
1049-3301
1-15
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
148cf072-b533-4168-9b4d-ffadfb69fb15
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
148cf072-b533-4168-9b4d-ffadfb69fb15

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. (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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A_Practical_Approach_to_Subset_Selection_with_Chance_Constraints_Author's version - Accepted Manuscript
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More information

Accepted/In Press date: 5 March 2021
e-pub ahead of print date: 16 August 2021
Published date: 1 October 2021
Keywords: Ranking and selection, simulation, subset selection, chance constraints

Identifiers

Local EPrints ID: 447867
URI: http://eprints.soton.ac.uk/id/eprint/447867
ISSN: 1049-3301
PURE UUID: 786b627f-0dda-4ede-b6c8-2fda0b4295b6
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652

Catalogue record

Date deposited: 25 Mar 2021 17:30
Last modified: 23 Oct 2021 04:01

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Contributors

Author: Thomas Monks

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