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

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Transportation research. Part E, Logistics and transportation review Ročník 163; s. 102749
Hlavní autori: Jahani, Hamed, Chaleshtori, Amir Eshaghi, Khaksar, Seyed Mohammad Sadegh, Aghaie, Abdollah, Sheu, Jiuh-Biing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier Ltd 01.07.2022
Predmet:
COVID-19 pandemic
Crisis-induced vaccine supply chain
Goal attainment optimisation
NSGA-II
Queuing system
COVID-19 pandemic
NSGA-II
Crisis-induced vaccine supply chain
Goal attainment optimisation
Queuing system
ISSN:1366-5545, 1878-5794, 1878-5794
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program. •Design a crisis-induced vaccine distribution network to order and allocate COVID-19 vaccines.•Consider a utility function for queues and a risk aversion coefficient for vaccinees.•Develop a bi-objective nonlinear programming model for the queuing model.•Control congestion by optimising the number of servers required in each hospital.•Develop an improved NSGA-II heuristic solution approach with high efficiency.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1366-5545
1878-5794
1878-5794
DOI:10.1016/j.tre.2022.102749