Dynamic medical logistics with drone-truck collaboration and pheromone decay in ant colony optimization

The timely and reliable delivery of medical supplies is vital in emergencies, where even minor delays can endanger lives. This study proposes a real-time optimisation framework for healthcare logistics that incorporates an urgency-weighted pheromone decay mechanism into an Ant Colony Optimization (ACO) algorithm. The method dynamically adjusts routing priorities according to medical urgency, enabling adaptive path selection under fluctuating traffic, battery, and weather conditions. A hybrid drone–truck collaboration system is developed, combining the endurance of trucks for long-range transport with the agility of drones for last-mile, time-critical deliveries. Simulation experiments using operational data from 40 healthcare facilities in Delhi demonstrate an approximately 30% reduction in average delivery time, a 15% decrease in fuel consumption, and an approximately 30% improvement in overall energy efficiency compared to conventional static routing models. These findings indicate that the proposed framework offers a scalable, energy-aware, and urgency-responsive solution for dynamic healthcare supply chains, with potential applicability to other critical logistics domains.