Queuing theory for efficient drone dispatch in healthcare logistics: An empirical analysis of system performance

Abstract

This study integrates queuing theory into drone-based healthcare logistics to address delivery delays, resource inefficiencies, and operational constraints in transporting time-sensitive medical supplies, including blood, organs, and vaccines. Traditional ground-based logistics often face traffic congestion, infrastructure limitations, and geographical barriers, compromising the timely delivery of medical resources. Drones offer a transformative solution by bypassing these obstacles, ensuring direct and efficient deliveries, particularly in remote and underserved regions. Leveraging queuing theory, this research develops a dynamic framework for real-time resource allocation, optimizing operational factors such as battery life, payload constraints, and fleet availability. Simulation results demonstrate that the proposed queuing-based drone system reduces delivery times by up to 60 %, improves resource utilization by 21 %, and enhances delivery success rates by 18 % compared to conventional logistics models. The model dynamically prioritizes urgent deliveries, adapts to fluctuating demand, and ensures resilience in critical interventions and disaster response scenarios. This study provides actionable insights into optimizing healthcare logistics, with potential enhancements through AI-driven demand prediction and infrastructure advancements. By establishing a scalable and efficient framework, this research contributes to modernizing healthcare supply chains, ensuring reliable access to medical supplies and improved patient outcomes globally.