The University of Southampton
University of Southampton Institutional Repository

A simulation methodology for continuous systems

A simulation methodology for continuous systems
A simulation methodology for continuous systems
This paper discusses the problem of modelling a continuous supply chain efficiently. Some existing modelling systems have poor performance, severely limiting their utility. The core of this work is the design, implementation and testing of a more efficient computational pattern that is claimed to improve performance. While the problem is apparently continuous, analysis suggests that this problem can be modelled using an adaption of discrete techniques. A pattern involving a modification of the Three Phase Approach discrete-event simulation technique was developed. Analysis of the way in which the effects of an event spread within the system modelled leads to a method by which excessive re-calculation can be avoided, yielding a model that is computationally more efficient. The pattern is then used in the investigation of automated design of the structure of the supply chain. The production, processing, transportation and consumption of Liquid National Gas (LNG) and the associated products form a complex supply chain and were selected as the example problem to be the subject of this work. The results demonstrate a high level of performance – sufficient speed to make experimentation with supply chain structure problems, with a real world level of complexity, practical.
Stchedroff, Niels
04e5abde-7406-468f-832c-52481c0e6ba5
Stchedroff, Niels
04e5abde-7406-468f-832c-52481c0e6ba5
Potts, C.N.
58c36fe5-3bcb-4320-a018-509844d4ccff

Stchedroff, Niels (2010) A simulation methodology for continuous systems. University of Southampton, School of Mathematics, Masters Thesis, 126pp.

Record type: Thesis (Masters)

Abstract

This paper discusses the problem of modelling a continuous supply chain efficiently. Some existing modelling systems have poor performance, severely limiting their utility. The core of this work is the design, implementation and testing of a more efficient computational pattern that is claimed to improve performance. While the problem is apparently continuous, analysis suggests that this problem can be modelled using an adaption of discrete techniques. A pattern involving a modification of the Three Phase Approach discrete-event simulation technique was developed. Analysis of the way in which the effects of an event spread within the system modelled leads to a method by which excessive re-calculation can be avoided, yielding a model that is computationally more efficient. The pattern is then used in the investigation of automated design of the structure of the supply chain. The production, processing, transportation and consumption of Liquid National Gas (LNG) and the associated products form a complex supply chain and were selected as the example problem to be the subject of this work. The results demonstrate a high level of performance – sufficient speed to make experimentation with supply chain structure problems, with a real world level of complexity, practical.

Text
thesis_revised_5.pdf - Other
Download (2MB)

More information

Published date: September 2010
Organisations: University of Southampton

Identifiers

Local EPrints ID: 167477
URI: http://eprints.soton.ac.uk/id/eprint/167477
PURE UUID: 19ac6ab4-1aa3-43fb-9332-f58831f71df1

Catalogue record

Date deposited: 25 Nov 2010 16:28
Last modified: 14 Mar 2024 02:15

Export record

Contributors

Author: Niels Stchedroff
Thesis advisor: C.N. Potts

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×