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.
Cao, Yiyun
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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)
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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
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Local EPrints ID: 449868
URI: http://eprints.soton.ac.uk/id/eprint/449868
PURE UUID: 3570280b-b0f1-4082-8c8e-bf563a10cccc
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Date deposited: 23 Jun 2021 16:31
Last modified: 17 Mar 2024 03:59
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Author:
Michael Higgins
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