COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia

Jahani, Hamed; Chaleshtori, Amir Eshaghi; Khaksar, Seyed Mohammad Sadegh; Aghaie, Abdollah; Sheu, Jiuh‐Biing

doi:10.1016/j.tre.2022.102749

Cited by 38 publications

(9 citation statements)

References 82 publications

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“…Jahani et al. [31] sought to efficiently and effectively distribute and store the COVID-19 vaccines, considering the congestion in the vaccination procedure. They conducted a multi-period queuing-based model to minimize the total cost and the expected waiting time.…”

Section: Literature Reviewmentioning

confidence: 99%

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A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

Wang

Jiang

2023

Omega

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Section: Literature Reviewmentioning

confidence: 99%

“… [62]

Vaccination coverage SP Jahani et al. [31]

Arrival&service rate Queue Georgiadis and Georgiadis [25]

Demand Rolling-horizon Fadaki et al. [23]

- - Azadi et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

Wang

Jiang

2023

Omega

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“…The percentages can be estimated by using COVID-19 historical data with a data analytics prediction model like classification, clustering, or time series (c.f. Jahani et al, 2022).…”

Section: Mathematical Modelmentioning

confidence: 99%

Viable healthcare supply chain network design for a pandemic

et al. 2022

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The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model’s applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

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“…Previous studies have also called for research in different facets of vaccine distribution in detail, namely product, production, assignment and delivery (Duijzer et al, 2018). Furthermore, although this is generally the case even for other VSCs, disrupted VSCs deserve special attention (De Boeck et al, 2020;Jahani et al, 2022). With instatement of the COVID-19 restrictions such as lockdowns, border closures and social distance measures, these VSCs experienced disruption in both supply of essential and nonessential supplies.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the experience of VSCs from different countries shows that the effectiveness in a disrupted VSC is strongly dependent on performance of many inter-related factors. These factors include the structure of the VSC networks (Moosavi et al, 2022), type and number of stakeholders involved (Talisuna et al, 2022), resources available for production and distribution (Moosavi et al, 2022), time required for ramping up commercial production (Jahani et al, 2022), duration and timing of facilities for vaccine administration (Ivanov, 2020), demand management during hospitalization (Ivanov and Das, 2020), transparency and trust between general public and the government (Harring et al, 2021;Alhawari et al, 2021;Rowan and Laffey, 2020). Furthermore, the effectiveness of vaccine administration also depends on the nature of the economy of the country.…”

Section: Introductionmentioning

confidence: 99%

Analysis of performance of Covid-19 vaccine supply chain in India

Nagarajan

Indira²,

Ramasubramaniam³

2022

JGOSS

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Purpose This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states. Design/methodology/approach This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites. Findings Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency. Research limitations/implications This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery. Originality/value The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

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COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia

Cited by 38 publications

References 82 publications

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

Viable healthcare supply chain network design for a pandemic

Analysis of performance of Covid-19 vaccine supply chain in India

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