Digital twin simulation for operational decision making
Digital twin simulation for operational decision making
Simulation models are commonly used as part of system design or management projects and are only now beginning to appear as part of operational control systems. When used to solve problems in real time, simulation models are typically described as being, or forming part of, a digital twin. Using a simulation model as part of a digital twin enables testing of different strategies or system settings based on the current state of the system. For this to be effective, results need to be returned quickly, requiring efficient algorithms for simulation optimization and effective use of data analytics and machine learning methods to complement the simulation modelling. Beginning with a discussion of digital twin models in the context of process simulation, we cover some of the key concepts that enable digital twins to support real-time decision-making, including metamodelling, multi-fidelity simulation optimization and simulation analytics.
Simulation, Digital Twin, simulation optimization, Metamodelling, simulation analytics, Digital twin, metamodelling
12-17
The Operational Research Society
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
2 April 2025
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Currie, Christine
(2025)
Digital twin simulation for operational decision making.
Harper, Alison, Luis, Martino, Monks, Thomas and Mustafee, Navonil
(eds.)
In Proceedings of the Operational Research Society Simulation Workshop 2025 (SW25).
The Operational Research Society.
.
(doi:10.36819/SW25.002).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Simulation models are commonly used as part of system design or management projects and are only now beginning to appear as part of operational control systems. When used to solve problems in real time, simulation models are typically described as being, or forming part of, a digital twin. Using a simulation model as part of a digital twin enables testing of different strategies or system settings based on the current state of the system. For this to be effective, results need to be returned quickly, requiring efficient algorithms for simulation optimization and effective use of data analytics and machine learning methods to complement the simulation modelling. Beginning with a discussion of digital twin models in the context of process simulation, we cover some of the key concepts that enable digital twins to support real-time decision-making, including metamodelling, multi-fidelity simulation optimization and simulation analytics.
Text
SW25_Keynote (post review)
- Accepted Manuscript
Restricted to Repository staff only
Request a copy
More information
Accepted/In Press date: 29 January 2025
Published date: 2 April 2025
Venue - Dates:
Simulation Workshop 2025, Mercure Rougemont, Exeter, United Kingdom, 2025-03-31 - 2025-04-02
Keywords:
Simulation, Digital Twin, simulation optimization, Metamodelling, simulation analytics, Digital twin, metamodelling
Identifiers
Local EPrints ID: 501420
URI: http://eprints.soton.ac.uk/id/eprint/501420
PURE UUID: 4012cb1a-ac3a-4658-9f50-947cef218d42
Catalogue record
Date deposited: 30 May 2025 16:55
Last modified: 03 Sep 2025 01:38
Export record
Altmetrics
Contributors
Editor:
Alison Harper
Editor:
Martino Luis
Editor:
Thomas Monks
Editor:
Navonil Mustafee
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
