Resilient COVID-19 vaccine supply chain: An optimization and simulation approach for multi-objective management
•Developed a multi-objective optimization-simulation model for resilient COVID-19 VSC.•Optimized VSC performance with realistic features using constrained MOGWO and NSGA-II.•Simulated disruption scenarios to enhance SC flexibility and vaccine distribution resilience.•Proposed strategies to optimize...
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| Vydáno v: | Transportation research. Part E, Logistics and transportation review Ročník 201; s. 104168 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.09.2025
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| ISSN: | 1366-5545 |
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| Abstract | •Developed a multi-objective optimization-simulation model for resilient COVID-19 VSC.•Optimized VSC performance with realistic features using constrained MOGWO and NSGA-II.•Simulated disruption scenarios to enhance SC flexibility and vaccine distribution resilience.•Proposed strategies to optimize vaccine coverage, reducing operational costs and waste.•Identified optimal hubs for ultra-cold storage to ensure efficient vaccine delivery.
This study develops a comprehensive optimization-simulation model to enhance the efficiency and resilience of the COVID-19 Vaccine Supply Chain Network (VSCN). The model integrates multi-objective, multi-product, and multi-period programming to address critical decisions related to location-allocation, inventory levels, safety stock, vaccine flow, and shortage, while considering stringent ultra-cold chain requirements for mRNA vaccines and wastage. Leveraging constrained multi-objective Grey Wolf Optimizer (MOGWO) and Non-Dominated Sorting Genetic Algorithm (NSGA-II), the study provides robust solutions for a wide range of problem sizes, demonstrating the superior performance of constrained MOGWO in solving high-dimensional constraints. The algorithms are fine-tuned using the Taguchi method, and their performance is validated through metrics such as MID, HV, SP, and NS, highlighting their respective strengths. A large-scale case study conducted in Iran using anyLogistix simulation software over 60 days evaluates the resiliency of the SC under disruption scenarios. Strategies like equipping specific hubs with ultra-cold chains and implementing a Min-Max inventory policy with an LTL transportation policy reduce total costs and enhance service levels. The findings emphasize the importance of demand fulfillment, waste reduction, and resource allocation, offering actionable insights for logistics managers and policymakers in the healthcare sector, such as dynamically adjusting vaccine distribution based on real-time demand, optimizing cold chain infrastructure placement, and prioritizing vaccination center locations based on accessibility and fair distribution considerations. This study further highlights the importance of fine-tuning delivery frequency to vaccination centers based on actual demand patterns, reducing operational costs related to storage, transportation, and wastage, improving VSC resilience to respond rapidly to disruptions, and provides strategies for minimizing operational costs, reducing wastage, and improving vaccine accessibility. This work significantly contributes to COVID-19 VSC management by presenting a holistic framework that addresses real-world complexities. |
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| AbstractList | •Developed a multi-objective optimization-simulation model for resilient COVID-19 VSC.•Optimized VSC performance with realistic features using constrained MOGWO and NSGA-II.•Simulated disruption scenarios to enhance SC flexibility and vaccine distribution resilience.•Proposed strategies to optimize vaccine coverage, reducing operational costs and waste.•Identified optimal hubs for ultra-cold storage to ensure efficient vaccine delivery.
This study develops a comprehensive optimization-simulation model to enhance the efficiency and resilience of the COVID-19 Vaccine Supply Chain Network (VSCN). The model integrates multi-objective, multi-product, and multi-period programming to address critical decisions related to location-allocation, inventory levels, safety stock, vaccine flow, and shortage, while considering stringent ultra-cold chain requirements for mRNA vaccines and wastage. Leveraging constrained multi-objective Grey Wolf Optimizer (MOGWO) and Non-Dominated Sorting Genetic Algorithm (NSGA-II), the study provides robust solutions for a wide range of problem sizes, demonstrating the superior performance of constrained MOGWO in solving high-dimensional constraints. The algorithms are fine-tuned using the Taguchi method, and their performance is validated through metrics such as MID, HV, SP, and NS, highlighting their respective strengths. A large-scale case study conducted in Iran using anyLogistix simulation software over 60 days evaluates the resiliency of the SC under disruption scenarios. Strategies like equipping specific hubs with ultra-cold chains and implementing a Min-Max inventory policy with an LTL transportation policy reduce total costs and enhance service levels. The findings emphasize the importance of demand fulfillment, waste reduction, and resource allocation, offering actionable insights for logistics managers and policymakers in the healthcare sector, such as dynamically adjusting vaccine distribution based on real-time demand, optimizing cold chain infrastructure placement, and prioritizing vaccination center locations based on accessibility and fair distribution considerations. This study further highlights the importance of fine-tuning delivery frequency to vaccination centers based on actual demand patterns, reducing operational costs related to storage, transportation, and wastage, improving VSC resilience to respond rapidly to disruptions, and provides strategies for minimizing operational costs, reducing wastage, and improving vaccine accessibility. This work significantly contributes to COVID-19 VSC management by presenting a holistic framework that addresses real-world complexities. |
| ArticleNumber | 104168 |
| Author | Solimanpur, Maghsud Khalilpoor, Saeedeh Kamran, Mehdi A. |
| Author_xml | – sequence: 1 givenname: Saeedeh surname: Khalilpoor fullname: Khalilpoor, Saeedeh email: saeedekhalilpoor@gmail.com organization: Faculty of Engineering, Urmia University, Urmia, Iran – sequence: 2 givenname: Mehdi A. orcidid: 0000-0002-1706-5172 surname: Kamran fullname: Kamran, Mehdi A. email: mehdi.kamran@gutech.edu.om organization: Department of Logistics, Tourism, and Services Management, German University of Technology in Oman, P.O. Box 1816, PC 130, Muscat, Oman – sequence: 3 givenname: Maghsud orcidid: 0000-0002-9765-9199 surname: Solimanpur fullname: Solimanpur, Maghsud email: m.solimanpur@urmia.ac.ir organization: Faculty of Engineering, Urmia University, Urmia, Iran |
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| Keywords | Optimization-simulation Multi-objective mixed-integer programming COVID-19 vaccine supply chain Grey wolf optimizer Non-dominated sorting genetic algorithm |
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| SubjectTerms | COVID-19 vaccine supply chain Grey wolf optimizer Multi-objective mixed-integer programming Non-dominated sorting genetic algorithm Optimization-simulation |
| Title | Resilient COVID-19 vaccine supply chain: An optimization and simulation approach for multi-objective management |
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