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Simulation optimization for a digital twin using a multi-fidelity framework

Simulation optimization for a digital twin using a multi-fidelity framework
Simulation optimization for a digital twin using a multi-fidelity framework
Digital twin technology is increasingly ubiquitous in manufacturing and there is a need to increase the efficiency of optimization methods that use digital twins to answer questions about the real system. The decisions that these methods support are typically short-term operational questions and, as a result, optimization methods need to return results in real or near-to-real time. This is especially challenging in manufacturing systems as the simulation models are typically large and complex. In this article, we describe an algorithm for a multi-fidelity model that uses a simpler low-fidelity neural network meta-model in the first stage of the optimization and a high-fidelity simulation model in the second stage. Some initial experimentation suggesting that it performs well.
IEEE Press
Cao, Yiyun
cfc96d2e-fd5c-44cf-95b1-71537a56964c
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Higgins, Michael
335b05bf-6883-47a7-9566-116ea70d52a5
Cao, Yiyun
cfc96d2e-fd5c-44cf-95b1-71537a56964c
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Higgins, Michael
335b05bf-6883-47a7-9566-116ea70d52a5

Cao, Yiyun, Currie, Christine, Onggo, Bhakti Stephan and Higgins, Michael (2021) Simulation optimization for a digital twin using a multi-fidelity framework. In Proceedings of the 2021 Winter Simulation Conference. IEEE Press.. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Digital twin technology is increasingly ubiquitous in manufacturing and there is a need to increase the efficiency of optimization methods that use digital twins to answer questions about the real system. The decisions that these methods support are typically short-term operational questions and, as a result, optimization methods need to return results in real or near-to-real time. This is especially challenging in manufacturing systems as the simulation models are typically large and complex. In this article, we describe an algorithm for a multi-fidelity model that uses a simpler low-fidelity neural network meta-model in the first stage of the optimization and a high-fidelity simulation model in the second stage. Some initial experimentation suggesting that it performs well.

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Accepted/In Press date: 9 June 2021

Identifiers

Local EPrints ID: 449868
URI: http://eprints.soton.ac.uk/id/eprint/449868
PURE UUID: 3570280b-b0f1-4082-8c8e-bf563a10cccc
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Bhakti Stephan Onggo: ORCID iD orcid.org/0000-0001-5899-304X

Catalogue record

Date deposited: 23 Jun 2021 16:31
Last modified: 24 Jun 2021 01:57

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Contributors

Author: Yiyun Cao
Author: Michael Higgins

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