Time-Critical Multimodal Medical Transportation: Organs, Patients, and Medical Supplies

TL;DR

This paper presents a heuristic algorithm for multimodal medical transportation, integrating ground and air vehicles to improve efficiency and reduce costs in urgent healthcare scenarios.

Contribution

It introduces a novel greedy dispatching algorithm for multimodal fleets, considering payload, traffic, and weather, and compares four fleet configurations for optimal medical transport.

Findings

Multimodal fleets outperform single-mode in efficiency and cost.

Integrated fleet with UAVs and eVTOLs reduces transportation time.

Heuristic approach enables rapid dispatching compared to optimization models.

Abstract

Timely transportation of organs, patients, and medical supplies is critical to modern healthcare, particularly in emergencies and transplant scenarios where even short delays can severely impact outcomes. Traditional ground-based vehicles such as ambulances are often hindered by traffic congestion; while air vehicles such as helicopters are faster but costly. Emerging air vehicles -- Unmanned Aerial Vehicles and electric vertical take-off and landing aircraft -- have lower operating costs, but remain limited by range and susceptibility to weather conditions. A multimodal transportation system that integrates both air and ground vehicles can leverage the strengths of each to enhance overall transportation efficiency. This study introduces a constructive greedy heuristic algorithm for multimodal vehicle dispatching for medical transportation. Four different fleet configurations were tested: (i) ambulances only, (ii) ambulances with Unmanned Aerial Vehicles, (iii) ambulances with electric vertical take-off and landing aircraft, and (iv) a fully integrated fleet of ambulances, Unmanned Aerial Vehicles, and electric vertical take-off and landing aircraft. The algorithm incorporates payload consolidation across compatible routes, accounts for traffic congestion in ground operations and weather conditions in aerial operations, while enabling rapid vehicle dispatching compared to computationally intensive optimization models. Using a common set of conditions, we evaluate all four fleet types to identify the most effective configurations for fulfilling medical transportation needs while minimizing operating costs, recharging/fuel costs, and total transportation time.