Session: Government Agency Student Posters

Paper Number: 173429

Drone-Enhanced Last-Mile Medical Delivery in Rural Areas: A Weather-Aware Optimization Approach 

Rural healthcare systems face persistent challenges in ensuring the timely delivery of medical supplies due to limited infrastructure, long travel distances, and adverse, variable environmental conditions. Such logistical barriers became especially pronounced during the COVID-19 pandemic, delaying access to critical medical resources and exposing vulnerabilities in traditional delivery systems. To improve healthcare accessibility and resilience in underserved regions, innovative last-mile delivery solutions are essential. While drones offer route flexibility and can bypass underdeveloped road networks, making them particularly effective in remote areas, their limited payload capacity and sensitivity to weather-dependent energy consumption present operational barriers.

This study proposes a weather-aware, multi-modal optimization framework that integrates drones and ground vehicles to design cost-effective, sustainable, and resilient medical logistics networks. The framework simultaneously optimizes routing, scheduling, energy management, and transshipment operations to minimize total system costs, including transportation, recharging, and operational expenses, while satisfying real-world constraints such as delivery time windows, energy thresholds, and payload capacity limits.

A major contribution of this work is the incorporation of dynamic, time-dependent, location-specific drone energy consumption directly into the optimization model.  Drone energy use is modeled as a function of drone type, payload, arc geometry, and weather conditions, including wind speed and direction, at the time of departure. To enable spatially localized energy estimation, the service area is partitioned using Voronoi tessellation around nearby weather stations, assigning each network node to a weather-informed subregion.

Forecasted weather data is used to estimate energy consumption on each arc in the transportation network, capturing both diurnal and spatial variability. For arcs that span multiple Voronoi regions, energy requirements are computed in segments and aggregated to ensure accuracy. A fitted predictive function transforms these segment-level forecasts into continuous time-dependent energy cost functions used as model inputs. This approach enables energy-aware scheduling decisions, such as adjusting departure times to take advantage of tailwinds or avoid energy-inefficient headwind conditions.

The model supports multi-modal coordination by allowing orders to transfer between drones and ground vehicles at candidate transshipment locations and by optimizing placement of recharging stations. Operational constraints such as time windows, payload capacities, energy thresholds, and recharge times are explicitly enforced, making the solution implementable in real-world settings. Applied to a case study based on Shield Illinois COVID-19 testing initiative, the model is evaluated across multiple delivery scenarios with varying demand levels and wind conditions. Results show that the proposed approach can reduce total costs by up to 85%, decrease emissions by more than 90%, and improve delivery timeliness compared to ground-only decentralized systems.

By systematically quantifying trade-offs among cost, energy consumption, delivery time, and feasibility, this framework provides decision-makers with a robust tool for optimizing rural medical supply chains under uncertainty and resource constraints. This methodology is broadly applicable to other time-sensitive healthcare delivery contexts, including diagnostics, emergency response, and vaccine distribution, particularly in underserved areas.

Presenting Author: Pardis Bahmani University of Missouri- St-Louis

Presenting Author Biography: Pardis Bahmani is a first year PhD student in the Supply Chain & Analytics Department at the University of Missouri–St. Louis. She holds a B.Sc. in Industrial Engineering from the University of Tabriz and an M.Sc. in Industrial Engineering–Systems Optimization from Iran University of Science and Technology.

Her research focuses on systems optimization and the design of resilient, efficient, and sustainable logistics networks, with applications in healthcare and renewable energy. Her current work develops advanced optimization frameworks for integrating drones into rural medical supply chains, addressing uncertainty in battery performance due to weather variability and its impact on network reliability. This research contributes to improving healthcare accessibility and delivery in underserved areas, aligning with societal impact, technological innovation, and infrastructure resilience.

Pardis presented her research at the 66th Transportation Research Forum (TRF), where she received the Best Paper Award. She is also a co-author of the peer-reviewed publication, “An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: a case study.” published in Renewable Energy journal in 2024.

Her long-term goal is to contribute to both academia and industry through interdisciplinary research that advances equitable and sustainable systems in healthcare and renewable energy to help enhancing human well being.

Authors:

Pardis Bahmani University of Missouri- St-Louis
Shakiba Enayati University of Missouri- St.Louis