Optimising vaccines supply chains to mitigate the COVID-19 pandemic

Rahman, HF; Chakrabortty, RK; Paul, SK; Elsawah, S

Optimising vaccines supply chains to mitigate the COVID-19 pandemic

Rahman, HF Chakrabortty, RK Paul, SK Elsawah, S

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Publisher:: Taylor and Francis Group
Publication Type:: Journal Article
Citation:: International Journal of Systems Science: Operations and Logistics, 2023, 10, (1), pp. 1-33
Issue Date:: 2023-03

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Field	Value	Language
dc.contributor.author	Rahman, HF
dc.contributor.author	Chakrabortty, RK
dc.contributor.author	Paul, SK
dc.contributor.author	Elsawah, S
dc.date.accessioned	2024-03-07T21:57:33Z
dc.date.available	2024-03-07T21:57:33Z
dc.date.issued	2023-03
dc.identifier.citation	International Journal of Systems Science: Operations and Logistics, 2023, 10, (1), pp. 1-33
dc.identifier.issn	2330-2674
dc.identifier.issn	2330-2682
dc.identifier.uri	http://hdl.handle.net/10453/176308
dc.description.abstract	A vaccination campaign is one of the most important initiatives to return life to normal in the face of the current COVID-19 epidemic. A successful immunisation programme requires optimising the strategy since uncertainty and disruptions play a role in the decision-making process. Despite the significance of this issue in practical terms, little research has been done to develop the best vaccine delivery strategy while considering uncertainties and interruptions. By developing bi-objective mathematical models that take into account both long-term (such as the allocation of vaccine types, the size of the vaccine centre, and the number of healthcare workers per vaccine centre) and short-term (such as daily order placement) decisions, we investigate the vaccine distribution problem for Canberra city in the Australian Capital Territory. The models also consider vaccination effectiveness and hesitation, disruptions in vaccine supplies, unpredictability in vaccine transportation, and loss of vaccines while handling them. Two meta-heuristic methods are created and put into use to solve these models. The effectiveness of the suggested algorithms is assessed using self-generated examples that were motivated by actual issues. The findings of this study will aid decision-makers in streamlining COVID-19 vaccine supply chains in the face of unpredictability and disruptions.
dc.language	en
dc.publisher	Taylor and Francis Group
dc.relation.ispartof	International Journal of Systems Science: Operations and Logistics
dc.relation.isbasedon	10.1080/23302674.2022.2122757
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0915 Interdisciplinary Engineering
dc.subject.classification	4901 Applied mathematics
dc.title	Optimising vaccines supply chains to mitigate the COVID-19 pandemic
dc.type	Journal Article
utslib.citation.volume	10
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 Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Management Discipline
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2024-03-07T21:57:30Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	1

Abstract:

A vaccination campaign is one of the most important initiatives to return life to normal in the face of the current COVID-19 epidemic. A successful immunisation programme requires optimising the strategy since uncertainty and disruptions play a role in the decision-making process. Despite the significance of this issue in practical terms, little research has been done to develop the best vaccine delivery strategy while considering uncertainties and interruptions. By developing bi-objective mathematical models that take into account both long-term (such as the allocation of vaccine types, the size of the vaccine centre, and the number of healthcare workers per vaccine centre) and short-term (such as daily order placement) decisions, we investigate the vaccine distribution problem for Canberra city in the Australian Capital Territory. The models also consider vaccination effectiveness and hesitation, disruptions in vaccine supplies, unpredictability in vaccine transportation, and loss of vaccines while handling them. Two meta-heuristic methods are created and put into use to solve these models. The effectiveness of the suggested algorithms is assessed using self-generated examples that were motivated by actual issues. The findings of this study will aid decision-makers in streamlining COVID-19 vaccine supply chains in the face of unpredictability and disruptions.

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http://hdl.handle.net/10453/176308