Innovative solutions for drone-based blood bag delivery to urban hospitals: a model and heuristic approach

Ensuring efficient and timely blood bag delivery is a critical challenge in urban healthcare logistics, where traditional transportation methods face traffic congestion, infrastructure limitations, and emergency response delays. This study proposes a drone-based logistics framework to optimize blood bag deliveries to metropolitan hospitals. A Mixed-Integer Linear Programming (MILP) model is developed to design an optimized delivery strategy considering urban constraints, drone operational limitations, and healthcare-specific logistical requirements. Given the computational complexity of MILP solvers, a block-stacking heuristic is introduced to enhance efficiency, enabling real-time decision-making and large-scale feasibility. The proposed model and heuristic are validated through numerical experiments across various problem instances. Results demonstrate that the heuristic outperforms traditional optimization solvers, achieving near-optimal solutions with an average optimality gap of ≤ 1% for small problems and ≤ 16% for larger cases. While GUROBI fails to solve large-scale problems within 12 h, the heuristic provides solutions in under 0.015 s, making it computationally superior and practically viable. The findings indicate that drone integration can improve delivery efficiency by up to 40%, reducing delays and mitigating congestion. This study highlights the transformative potential of drones in medical logistics, offering a scalable, adaptive, and computationally efficient approach for healthcare supply chains. Introduces a novel block-stacking heuristic that reduces computation time (≤ 0.015 s) for large-scale drone delivery problems, ensuring practical real-time application in urban healthcare systems. Develops a detailed Mixed-Integer Linear Programming (MILP) model considering payload, battery, time, and urban constraints to optimize blood bag deliveries to metropolitan hospitals. Demonstrates that the heuristic maintains a ≤ 1% optimality gap in small problems and ≤ 16% in large ones, while solvers like GUROBI fail beyond medium-sized instances (60 + hospitals). Validated with 60 instances based on real hospital locations in Delhi, India, proving the model’s scalability from 10 to 80 hospitals and drone fleets ranging from 2 to 16 units. Shows potential to improve delivery efficiency by up to 40%, offering a sustainable, congestion-free solution for time-critical medical logistics using drones in smart city environments.