IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v193y2025ics1366554524004228.html
   My bibliography  Save this article

The role of drone technology and application of IoT on vaccine supply chain during a pandemic under uncertain Environment: A real case study of COVID-19 in Iran

Author

Listed:
  • Ansari, Nadia
  • Fattahi, Parviz
  • Shiri, Mahdyeh

Abstract

Vaccination is a crucial way to combat the pandemic; in other words, vaccines play an important role in controlling the spread of the virus and ultimately ending the pandemic. This study presents a multi-objective mixed integer linear programming model for the vaccine supply chain considering uncertain cost, vaccine purchase, and lead time. Through the utilization of Internet of Things technology, data about various groups is collected. Upon identification of individuals with good health, the specific needs of each area are ascertained during each period. Subsequently, a mathematical model for the vaccine supply chain is presented, encompassing four distinct levels; manufacturers, distribution centers, health centers, and immunization centers. Furthermore, this model incorporates the utilization of drones to deliver vaccines from distribution centers to health centers because of the significant distance between these two levels. The proposed framework encompasses two main goals; minimizing the total cost and the waiting time for people in the queue. A novel fuzzy approach has been employed to deal with the uncertain parameters. The model’s validation is accomplished through the implementation of a real case study of COVID-19 in Iran. The findings indicate that the lack of Internet of Things technology implementation results in a higher number of individuals being directed to immunization centers, thereby elevating the likelihood of infection, and, this scenario leads to the unnecessary administration of vaccines, leading to resource wastage. Additionally, without using drones, vaccines cannot be delivered and injected into people on time. Ultimately, the proposed framework and methodology can be applied in almost larger dimensions and the results demonstrate the model and methods’ efficiency and effectiveness. Since this study is applied to the case study of COVID-19, the findings can be applied in the conditions of similar pandemics.

Suggested Citation

  • Ansari, Nadia & Fattahi, Parviz & Shiri, Mahdyeh, 2025. "The role of drone technology and application of IoT on vaccine supply chain during a pandemic under uncertain Environment: A real case study of COVID-19 in Iran," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:transe:v:193:y:2025:i:c:s1366554524004228
    DOI: 10.1016/j.tre.2024.103831
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524004228
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103831?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kochakkashani, Farid & Kayvanfar, Vahid & Haji, Alireza, 2023. "Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    2. Fariba Goodarzian & Ali Navaei & Behdad Ehsani & Peiman Ghasemi & Jesús Muñuzuri, 2023. "Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions," Annals of Operations Research, Springer, vol. 328(1), pages 531-575, September.
    3. Wang, Xin & Jiang, Ruiwei & Qi, Mingyao, 2023. "A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply," Omega, Elsevier, vol. 119(C).
    4. Fahimnia, Behnam & Jabbarzadeh, Armin, 2016. "Marrying supply chain sustainability and resilience: A match made in heaven," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 306-324.
    5. Dario Floreano & Robert J. Wood, 2015. "Science, technology and the future of small autonomous drones," Nature, Nature, vol. 521(7553), pages 460-466, May.
    6. Dastgoshade, Sohaib & Shafiee, Mohammad & Klibi, Walid & Shishebori, Davood, 2022. "Social equity-based distribution networks design for the COVID-19 vaccine," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Kamran, Mehdi A. & Kia, Reza & Goodarzian, Fariba & Ghasemi, Peiman, 2023. "A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    8. Armin Jabbarzadeh & Behnam Fahimnia & Fatemeh Sabouhi, 2018. "Resilient and sustainable supply chain design: sustainability analysis under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5945-5968, September.
    9. Jahani, Hamed & Chaleshtori, Amir Eshaghi & Khaksar, Seyed Mohammad Sadegh & Aghaie, Abdollah & Sheu, Jiuh-Biing, 2022. "COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    10. Hyun Seop Uhm & Young Hoon Lee, 2022. "Vehicle routing problem under safe separation distance for multiple unmanned aerial vehicle operation," Operational Research, Springer, vol. 22(5), pages 5107-5136, November.
    11. Fadaki, Masih & Abareshi, Ahmad & Far, Shaghayegh Maleki & Lee, Paul Tae-Woo, 2022. "Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    12. Sube Singh & Ramesh Kumar & Rohit Panchal & Manoj Kumar Tiwari, 2021. "Impact of COVID-19 on logistics systems and disruptions in food supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 1993-2008, April.
    13. M. Ehrgott & S. Ruzika, 2008. "Improved ε-Constraint Method for Multiobjective Programming," Journal of Optimization Theory and Applications, Springer, vol. 138(3), pages 375-396, September.
    14. Maria Elena Bruni & Sara Khodaparasti, 2022. "A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    15. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    16. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    17. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maleki, Abolfazl & Hemmati, Vahid & Reza Abazari, Seyed & Aghsami, Amir & Rabbani, Masoud, 2024. "Optimal distribution and waste management of Covid-19 vaccines from vaccination centers’ satisfaction perspective – A fuzzy time window-based VRP," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. Dukkanci, Okan & Campbell, James F. & Kara, Bahar Y., 2024. "Facility location decisions for drone delivery: A literature review," European Journal of Operational Research, Elsevier, vol. 316(2), pages 397-418.
    3. Sengul Orgut, Irem & Freeman, Nickolas & Lewis, Dwight & Parton, Jason, 2023. "Equitable and effective vaccine access considering vaccine hesitancy and capacity constraints," Omega, Elsevier, vol. 120(C).
    4. Wang, Xin & Jiang, Ruiwei & Qi, Mingyao, 2023. "A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply," Omega, Elsevier, vol. 119(C).
    5. Nishant Saravanan & Jessica Olivares-Aguila & Alejandro Vital-Soto, 2022. "Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains," Sustainability, MDPI, vol. 14(23), pages 1-33, November.
    6. Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Behnam Karimi, 2023. "Resilient and social health service network design to reduce the effect of COVID-19 outbreak," Annals of Operations Research, Springer, vol. 328(1), pages 903-975, September.
    7. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    8. Mohammadi, Mehrdad & Asgari, Erfan & Mamaghan, Maryam Karimi & Pirayesh, Amir & van Woensel, Tom, 2024. "Heterogeneous joint vaccine allocation and quarantine restriction planning under uncertainty: The COVID-19 pandemic," Omega, Elsevier, vol. 128(C).
    9. Pirayesh, Amir & Asadaraghi, Alireza & Mohammadi, Mehrdad & Siadat, Ali & Battaïa, Olga, 2025. "A dynamic optimization model for vaccine allocation with age considerations: A study inspired by the COVID-19 pandemic," International Journal of Production Economics, Elsevier, vol. 280(C).
    10. Maria Ghufran & Khurram Iqbal Ahmad Khan & Fahim Ullah & Wesam Salah Alaloul & Muhammad Ali Musarat, 2022. "Key Enablers of Resilient and Sustainable Construction Supply Chains: A Systems Thinking Approach," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    11. Quintero Gutiérrez, Laura Victoria & García Rendón, John & Gutiérrez Gómez, Alejandro, 2024. "Impact of COVID-19 preventive measures on electricity demand: Evidence from Colombia," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    12. Luis Francisco López-Castro & Elyn L. Solano-Charris, 2021. "Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    13. El-Awady Attia & Ali Alarjani & Md. Sharif Uddin & Ahmed Farouk Kineber, 2023. "Determining the Stationary Enablers of Resilient and Sustainable Supply Chains," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    14. Karakaya, Sırma & Balcik, Burcu, 2024. "Developing a national pandemic vaccination calendar under supply uncertainty," Omega, Elsevier, vol. 124(C).
    15. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    16. Bhavya Sharma & Murari Lal Mittal & Gunjan Soni & Bharti Ramtiyal, 2023. "An Implementation Framework for Resiliency Assessment in a Supply Chain," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 591-614, December.
    17. Jiang, Jiehui & Sheng, Dian & Chen, Xiaojing & Tian, Qiong & Li, Feng & Yang, Peng, 2024. "Data-driven collaborative healthcare resource allocation in pandemics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    18. Olfati, Marjan & Paydar, Mohammad Mahdi, 2023. "Towards a responsive-sustainable-resilient tea supply chain network design under uncertainty using big data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    19. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    20. Zahra Homayouni & Mir Saman Pishvaee & Hamed Jahani & Dmitry Ivanov, 2023. "A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 395-435, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:193:y:2025:i:c:s1366554524004228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.