Comparing conventional and distributed approaches to simulation in a complex supply-chain health system
Comparing conventional and distributed approaches to simulation in a complex supply-chain health system
Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today’s powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models.
To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period.
However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations
distributed simulation, supply chain systems, healthcare operations, simulation software, simul8
43-51
Katsaliaki, K.
23e62834-f8a7-490b-8b1e-62727fa20914
Mustafee, N.
717be2a5-36d8-4177-b945-fae19faa1b22
Taylor, S.J.E.
3db7259f-b299-49a0-825d-0e55d95f7b9e
Brailsford, S.C.
634585ff-c828-46ca-b33d-7ac017dda04f
January 2009
Katsaliaki, K.
23e62834-f8a7-490b-8b1e-62727fa20914
Mustafee, N.
717be2a5-36d8-4177-b945-fae19faa1b22
Taylor, S.J.E.
3db7259f-b299-49a0-825d-0e55d95f7b9e
Brailsford, S.C.
634585ff-c828-46ca-b33d-7ac017dda04f
Katsaliaki, K., Mustafee, N., Taylor, S.J.E. and Brailsford, S.C.
(2009)
Comparing conventional and distributed approaches to simulation in a complex supply-chain health system.
Journal of the Operational Research Society, 60 (1), .
(doi:10.1057/palgrave.jors.2602531).
Abstract
Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today’s powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models.
To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period.
However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations
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Published date: January 2009
Keywords:
distributed simulation, supply chain systems, healthcare operations, simulation software, simul8
Organisations:
Nano-Scale Integration Group
Identifiers
Local EPrints ID: 147551
URI: http://eprints.soton.ac.uk/id/eprint/147551
ISSN: 0160-5682
PURE UUID: 042d7973-a4f9-41e9-a6ae-f67fd2db2c6c
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Date deposited: 19 May 2010 07:33
Last modified: 14 Mar 2024 02:35
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Contributors
Author:
K. Katsaliaki
Author:
N. Mustafee
Author:
S.J.E. Taylor
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