Designing an efficient vaccine supply chain network using a two-phase optimization approach: a case study of COVID-19 vaccine

Abstract

The upsurge of the COVID-19 pandemic and its consequences on the world’s population has led to the accelerated production of vaccines across the world. The next challenge in the fight against COVID-19 is to distribute those vaccines to the right person in an optimized way by establishing an efficient vaccine supply network. Consequently, this paper studies an integrated two-phase planning framework for the vaccine distribution network. In the first phase, the population target is classified into several groups to determine their priority for vaccination using a multiple attribute decision making (MADM) technique. The second phase uses a mathematical model to decide on the location of distribution centers, inventory policies, and routing decisions to minimize the total procurement, inventory, and distribution costs. Since the proposed model falls into the category of NP-hard problems, a metaheuristic algorithm entitled population-based simulated annealing (PBSA) is developed to solve the presented model. Besides, the improved grey wolf optimizer (I-GWO) is customized and used as another solution method. A few generated datasets are utilized to evaluate and validate the performance of these solution methods. In addition, this methodology is implemented in a case study of the COVID-19 vaccine supply chain in Iran to demonstrate its practicability.

KEYWORDS:

• Vaccine supply chain
• stochastic model
• optimization
• metaheuristics
• logistics design
• vaccine delivery

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Readers have access to the generated datasets using the following URL: https://www.unsw.adfa.edu.au/dsar-group/dsarg-datasets. This URL is a publicly available repository to read, download and reuse data. Datasets used and developed for this study are classified according to the number of SDSs (J) and MCs (K). For example, consider the file named J18-K73 as a problem consisting of 18 SDSs and 73 MCs.

Additional information

Notes on contributors

Farhad Habibi

Farhad Habibi is currently doing his PhD in project management at the University of New South Wales, Canberra, Australia. He achieved his master's degree in Industrial Engineering (Systems Optimization) from the Iran University of Science and Technology in 2017. He focuses on mathematical modelling and optimisation techniques in supply chain and scheduling problems. His research interests mainly include multi-objective optimisation, optimisation under uncertainty, supply chain resilience, decision-making in transportation systems, sustainable design, and project scheduling.

Alireza Abbasi

Dr Alireza Abbasi is the Coordinator of Project Management programs in the University of New South Wales (UNSW) at Canberra. He obtained his PhD in Project Management from the University of Sydney in 2012 before joining the School of Engineering and Information Technology (SEIT) at UNSW Canberra. He also holds a graduate certificate in University Learning and Teaching from UNSW Sydney. His research and teaching interests include project management, management science, information systems management and network science. He has written a book, three book chapters and over 100 technical journal and conference papers.

Ripon Kumar Chakrabortty

Dr Ripon K. Chakrabortty (Senior Member, IEEE) is a Lecturer in Systems Engineering & Decision Analytics at the School of Engineering & Information Technology, UNW Canberra, Australia. He is the team leader and founder of ‘The Decision Support & Analytics Research Group’ at the School of Engineering & Information Technology, UNW Canberra, Australia. He serves as the program coordinator for Master of Decision Analytics & Master of Engineering in UNSW Canberra. He has written two book chapters and over 165 technical journal and conference papers in multiple prestigious venues. His research interest covers a wide range of topics in Operations Research, Project Management, Supply Chain Management, Artificial Intelligence, and Information Systems Management. Many organisations have funded his research program, such as the Department of Defence, Commonwealth Government, Australia.