Drone-Assisted Last-Mile Delivery: A Priority-Based Framework for Post-Disaster Logistics

 

 

 

In recent years, global disruptions such as natural disasters, pandemics, and conflicts have highlighted the vulnerability of traditional logistics systems, particularly in the critical final leg of delivery—commonly referred to as the “last mile.” In such scenarios, the local supply chain often collapses due to infrastructure damage, increased demand, or health risks, causing substantial delays in the distribution of essential goods.

Against this backdrop, the integration of unmanned aerial vehicles (UAVs), or drones, into delivery networks presents an innovative solution for ensuring the timely delivery of critical supplies. A recent study by Al Daqqa et al. (2025) proposes a comprehensive decision-making framework that prioritizes drone-assisted last-mile deliveries in such crisis situations.

Context and Motivation

In the aftermath of a disaster, communities frequently face acute shortages of life-essential items such as medicine, medical equipment, food, and water. Simultaneously, there may also be ongoing demand for non-essential items, including household goods and consumer products. When logistics systems are under strain, indiscriminate fulfillment of all deliveries can compromise the timely delivery of vital resources. Recognizing this, the authors of the study advocate for a structured prioritization scheme that allocates delivery resources based on the urgency and criticality of shipments.

Drones offer several advantages in this context. First, they enable contactless delivery, which is essential during health crises such as pandemics. Second, their mobility and flexibility allow them to access areas that are unreachable by ground vehicles due to road blockages or infrastructural damage. These features make drones particularly well-suited for post-disaster logistics.

The Proposed Framework

The study introduces a mixed integer programming (MIP) model to optimize delivery scheduling based on shipment priority. The framework categorizes shipments into three levels of priority:

• High Priority: Includes life-saving or urgent items that must be delivered immediately.

• Medium Priority: Consists of important but less time-sensitive goods that must be delivered within a specified time window.

• Low Priority: Refers to non-essential goods that can be delivered only after higher-priority demands are met.

The model determines the optimal allocation and routing of drones, ensuring that delivery deadlines are respected based on shipment priority while minimizing total operational costs and delays. By formalizing this prioritization within an optimization model, the framework helps ensure that limited drone and logistics resources are deployed where they are most needed.

Numerical Experiments and Findings

To validate the proposed approach, the authors conducted extensive numerical simulations under various disaster-response scenarios. The results demonstrated that incorporating shipment prioritization significantly improved the delivery performance of essential items, especially under resource-constrained conditions. Compared to non-prioritized delivery strategies, the framework reduced average delivery times for high-priority items and increased the overall effectiveness of drone deployment.

These findings reinforce the notion that prioritization is not only a practical necessity in crisis logistics but also a tractable optimization problem that can be solved with modern operations research techniques. The study also highlights the scalability of the model, indicating that it can be adapted for varying numbers of drones, delivery nodes, and demand scenarios.

Broader Implications

The implications of this research extend beyond disaster relief. The proposed prioritization framework can be applied to other high-demand contexts, such as holiday shopping seasons, urban congestion events, or remote medical supply chains. As drones become increasingly integrated into commercial delivery systems, frameworks like the one proposed by Al Daqqa et al. will be critical for managing competing delivery demands efficiently and ethically.

Moreover, the study contributes to the growing field of humanitarian logistics, where timely decision-making and resource allocation can directly affect human lives. By providing a model that balances operational efficiency with ethical responsibility, the authors offer a meaningful advancement in disaster-response logistics.

 References

 

• Murray, C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86–109.

• Rabta, B., Wankmüller, C., & Reiner, G. (2018). A drone fleet model for last-mile distribution in disaster relief operations. International Journal of Disaster Risk Reduction, 28, 107–112.

• Chung, S., Sah, B., & Lee, J. (2020). Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. Computers & Operations Research, 123, 105004.