A Multi-Objective Optimization Framework for Pandemic Vaccine Distribution Using IoT and Drones Under Uncertainty


 

 The COVID-19 pandemic has underscored the vital role of vaccination in mitigating the spread of infectious diseases and ultimately curtailing global health crises. Efficient vaccine distribution, particularly in conditions of uncertainty and logistical complexity, has become an essential component in managing public health emergencies.

In this context, the integration of advanced technologies such as the Internet of Things (IoT) and unmanned aerial vehicles (drones) can significantly enhance the performance of the vaccine supply chain. This study introduces a comprehensive and innovative multi-objective mixed-integer linear programming (MILP) model designed to optimize vaccine distribution in uncertain and dynamic environments, with a specific focus on minimizing total operational costs and waiting times for individuals at immunization centers.

The proposed model accounts for several key uncertainties prevalent in real-world supply chains, including fluctuating costs, variable vaccine procurement quantities, and uncertain lead times. These uncertainties are addressed using a novel fuzzy logic-based approach, enabling the model to operate effectively in the face of incomplete or imprecise information. The supply chain framework consists of four interconnected hierarchical levels: vaccine manufacturers, distribution centers, health centers, and immunization centers. Each level plays a critical role in ensuring the efficient and timely delivery of vaccines to the population.

A distinctive feature of this model is its incorporation of IoT technology to gather real-time data about population health status and logistical conditions. By leveraging IoT-enabled sensors and data analytics, the system identifies individuals who are in good health and determines the vaccine demand across different regions during each planning period. This information significantly enhances the responsiveness and precision of the distribution strategy, ensuring that vaccine resources are allocated where they are most needed.

Another notable innovation of the model is the deployment of drones to transport vaccines between distribution centers and health centers. This logistical enhancement addresses the challenge of long distances between these nodes, which often hinders timely vaccine delivery in conventional supply chains. Drones not only reduce transportation time but also help maintain the cold chain required for vaccine viability, thereby increasing the overall reliability of the distribution process.

The model was validated using a real-world case study of COVID-19 vaccine distribution in Iran. The results demonstrate that the use of IoT technology substantially reduces the number of unnecessary visits to immunization centers. In the absence of such technology, individuals may be directed to centers without proper assessment, thereby increasing the risk of virus transmission and leading to inefficient use of limited vaccine supplies. Similarly, the findings underscore the importance of drones in ensuring that vaccines reach remote areas in a timely manner. Without drone-based delivery, delays occur, compromising the effectiveness of immunization campaigns and potentially allowing the virus to spread further.

Overall, the proposed framework proves to be both efficient and scalable, with potential applicability to other large-scale vaccination efforts and future pandemics. By integrating modern technologies and robust optimization techniques, this study provides valuable insights into improving healthcare logistics under crisis conditions. The approach not only enhances operational efficiency but also supports public health objectives by facilitating equitable and timely access to vaccines. The methodology and findings of this research offer a significant contribution to the field of healthcare operations management and pandemic preparedness, suggesting practical strategies for optimizing vaccine distribution in the face of uncertainty.

 References

Minoza, J. M. A., Bongolan, V. P., & Rayo, J. F. (2021). COVID-19 Agent-Based Model with Multi-objective Optimization for Vaccine Distribution. 

Kolter, M., Eksioglu, S. D., Pinkley, S. N., & Proano, R. A. (2022). Designing Drone Delivery Networks for Vaccine Supply Chain: A Case Study of Niger

Dey, S., Kurbanzade, A. K., Gel, E. S., Mihaljevic, J., & Mehrotra, S. (2023). Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities

 

 

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