Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation

Khodabandeh-Yalabadi, A; Sheikhalishahi, M; Torabi, SA; Naderpour, M; Radmankian, AH

Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation

Khodabandeh-Yalabadi, A Sheikhalishahi, M Torabi, SA Naderpour, M

Radmankian, AH

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: International Journal of Systems Science: Operations and Logistics, 2024, 11, (1)
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Khodabandeh-Yalabadi, A
dc.contributor.author	Sheikhalishahi, M
dc.contributor.author	Torabi, SA
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875
dc.contributor.author	Radmankian, AH
dc.date.accessioned	2025-01-30T04:57:38Z
dc.date.available	2025-01-30T04:57:38Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Systems Science: Operations and Logistics, 2024, 11, (1)
dc.identifier.issn	2330-2674
dc.identifier.issn	2330-2682
dc.identifier.uri	http://hdl.handle.net/10453/184636
dc.description.abstract	Motivated by the critical need for supply chains to withstand disruptions, this paper investigates the application of three core resilience strategies and their combinations at the network design level. We propose an investigation framework that utilises Monte Carlo simulation to generate disruption rates of varying severity. This allows for the mathematical optimisation of eight different models, each representing a combination of the three core resilience strategies, based on the simulated disruptions. The framework also incorporates farmed structured data analysis to examine the performance and features of each model. Considering disruptions that can affect both suppliers and factories, a detailed comparison is performed between the strategies. Numerical comparisons reveal that the model incorporating all resilience strategies (Multiple Allocation Network with Backup Suppliers and Factories, or MANBSF) outperforms others, potentially achieving zero shortages even under significant disruptions. This comprehensive model allows for multiple allocations of facilities and incorporates backup suppliers and factories. The analysis further suggests that network resilience increases with size, density, and the number of complete paths.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	International Journal of Systems Science: Operations and Logistics
dc.relation.isbasedon	10.1080/23302674.2024.2398573
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0915 Interdisciplinary Engineering
dc.subject.classification	4901 Applied mathematics
dc.title	Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0102 Applied Mathematics
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)/Australian Artificial Intelligence Institute (AAII) Associate Members
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-30T04:57:36Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	1

Abstract:

Motivated by the critical need for supply chains to withstand disruptions, this paper investigates the application of three core resilience strategies and their combinations at the network design level. We propose an investigation framework that utilises Monte Carlo simulation to generate disruption rates of varying severity. This allows for the mathematical optimisation of eight different models, each representing a combination of the three core resilience strategies, based on the simulated disruptions. The framework also incorporates farmed structured data analysis to examine the performance and features of each model. Considering disruptions that can affect both suppliers and factories, a detailed comparison is performed between the strategies. Numerical comparisons reveal that the model incorporating all resilience strategies (Multiple Allocation Network with Backup Suppliers and Factories, or MANBSF) outperforms others, potentially achieving zero shortages even under significant disruptions. This comprehensive model allows for multiple allocations of facilities and incorporates backup suppliers and factories. The analysis further suggests that network resilience increases with size, density, and the number of complete paths.

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http://hdl.handle.net/10453/184636