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Tutorial on optimisation via simulation: How to choose the best set up for a system

Tutorial on optimisation via simulation: How to choose the best set up for a system
Tutorial on optimisation via simulation: How to choose the best set up for a system

In this tutorial we consider the problem of finding the best set up to use for a system, where the objective is measured using the output of a stochastic simulation model. What makes this a difficult problem is that the output is stochastic and consequently changes in each replication. Optimisation via simulation is a vast topic and we restrict ourselves to a small part of it-ranking and selection-in which a small number of discrete options are being compared. We describe two of the best-used methods, KN++ and OCBA. In these algorithms, just one solution is returned at the end of the optimisation and there is a single objective. We also discuss variations including best subset selection, multi-objective optimisation via simulation, and the minimisation of the expected opportunity cost. The tutorial is accompanied by a Github repository which includes Python code for the algorithms we describe here.

Optimisation via simulation, Ranking and selection, Simulation
35-41
Operational Research Society
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
fece343c-106d-461d-a1dd-71c1772627ca
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
fece343c-106d-461d-a1dd-71c1772627ca
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan

Currie, Christine and Monks, Thomas (2021) Tutorial on optimisation via simulation: How to choose the best set up for a system. Fakhimi, Masoud, Boness, Tom and Robertson, Duncan (eds.) In Operational Research Society 10th Simulation Workshop, SW 2021 - Proceedings. Operational Research Society. pp. 35-41 . (doi:10.36819/SW21.004).

Record type: Conference or Workshop Item (Paper)

Abstract

In this tutorial we consider the problem of finding the best set up to use for a system, where the objective is measured using the output of a stochastic simulation model. What makes this a difficult problem is that the output is stochastic and consequently changes in each replication. Optimisation via simulation is a vast topic and we restrict ourselves to a small part of it-ranking and selection-in which a small number of discrete options are being compared. We describe two of the best-used methods, KN++ and OCBA. In these algorithms, just one solution is returned at the end of the optimisation and there is a single objective. We also discuss variations including best subset selection, multi-objective optimisation via simulation, and the minimisation of the expected opportunity cost. The tutorial is accompanied by a Github repository which includes Python code for the algorithms we describe here.

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More information

Published date: 22 March 2021
Additional Information: Publisher Copyright: © 2021 SW 2021. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 10th Operational Research Society Simulation Workshop, SW 2021, , Virtual, Online, 2021-03-22 - 2021-03-26
Keywords: Optimisation via simulation, Ranking and selection, Simulation

Identifiers

Local EPrints ID: 449947
URI: http://eprints.soton.ac.uk/id/eprint/449947
PURE UUID: 7b9bbe07-db91-46d5-96e8-553386d00bbf
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Thomas Monks: ORCID iD orcid.org/0000-0003-2631-4481

Catalogue record

Date deposited: 28 Jun 2021 16:32
Last modified: 26 Nov 2021 02:46

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Contributors

Author: Thomas Monks ORCID iD
Editor: Masoud Fakhimi
Editor: Tom Boness
Editor: Duncan Robertson

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