Modeling a sustainable vaccine supply chain for a healthcare system

Chowdhury, NR; Ahmed, M; Mahmud, P; Paul, SK; Liza, SA

Modeling a sustainable vaccine supply chain for a healthcare system

Chowdhury, NR Ahmed, M Mahmud, P Paul, SK Liza, SA

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Cleaner Production, 2022, 370, pp. 1-19
Issue Date:: 2022-10

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Field	Value	Language
dc.contributor.author	Chowdhury, NR
dc.contributor.author	Ahmed, M
dc.contributor.author	Mahmud, P
dc.contributor.author	Paul, SK
dc.contributor.author	Liza, SA
dc.date.accessioned	2022-11-18T02:26:45Z
dc.date.available	2022-08-01
dc.date.available	2022-11-18T02:26:45Z
dc.date.issued	2022-10
dc.identifier.citation	Journal of Cleaner Production, 2022, 370, pp. 1-19
dc.identifier.issn	0959-6526
dc.identifier.issn	1879-1786
dc.identifier.uri	http://hdl.handle.net/10453/163585
dc.description.abstract	This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network’s economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics—namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods—are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.
dc.format	Print-Electronic
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Cleaner Production
dc.relation.isbasedon	10.1016/j.jclepro.2022.133423
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0907 Environmental Engineering, 0910 Manufacturing Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Environmental Sciences
dc.title	Modeling a sustainable vaccine supply chain for a healthcare system
dc.type	Journal Article
utslib.citation.volume	370
utslib.location.activity	Netherlands
utslib.for	0907 Environmental Engineering
utslib.for	0910 Manufacturing Engineering
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2022-11-18T02:26:38Z
pubs.publication-status	Published
pubs.volume	370

Abstract:

This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network’s economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics—namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods—are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.

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http://hdl.handle.net/10453/163585