Optimization and Engineering Analysis of A Drone-Based Blood Delivery Logistics System for Remote District Hospitals in Ulaanbaatar

Introduction

The lack of adequate access to health care services in the remote districts of Ulaanbaatar is a common challenge in urbanization in developing countries. In particular, the delivery of urgent medical supplies, such as blood and blood products, is hampered by traffic congestion and infrastructure limitations, is highly dependent on weather conditions, and, when delayed, poses a serious risk to patients' lives [1], [2]. Traditional ground transportation systems are inefficient under these conditions, and new solutions for rapid and reliable delivery are needed.

In recent years, uncrewed aerial vehicles (UAVs), or drone technology, have been widely adopted in the logistics, transportation, and healthcare sectors, particularly in remote regions. UAV (uncrewed aerial vehicle) or drone technology has been widely adopted in logistics, transportation, and healthcare, and numerous tudies have demonstrated its effectiveness in delivering medical supplies, blood, and vaccines to remote areas [3]–[6]. Using drones offers advantages such as shorter delivery times, reduced traffic congestion, and service in inaccessible areas.

For example, in countries such as Rwanda and Ghana, drone-based medical delivery systems have been implemented in real-world settings, achieving a 50–80% reduction in delivery times for blood, vaccines, and medical supplies [7], [8]. Moreover, Amukele et al. [9] found that transporting blood and blood products by drone does not adversely affect their quality and can maintain the required temperature regimes.

Moreover, issues such as optimization of drone-based logistics systems, route planning, and energy efficiency have become important research directions. Dorling et al. [3] and Murray & Chu [4] demonstrated the feasibility of optimizing drone delivery routes using mathematical modeling, while Chen et al. [10] developed a model for an energy-efficient UAV system.

However, these studies were mostly conducted in countries with well-developed infrastructure and relatively stable weather conditions, and applying them directly to Mongolia's conditions faces certain challenges. Mongolia's harsh climate, strong winds, and low temperatures significantly impact drone flight stability, battery performance, and safety [11], [12].

Therefore, there is a need to develop engineering solutions and optimization models tailored to the environment's specific characteristics. In this study, the engineering feasibility of a drone-based logistics system for transporting blood and blood products in a remote district of Ulaanbaatar is investigated. Delivery time, cost, and energy consumption are examined, and an optimal model is developed to reduce them.

Research Methodology

Research model

In this study, mathematical modeling and simulation methods were used to evaluate the performance of a drone-based logistics system for delivering blood and blood products to hospitals in remote districts of Ulaanbaatar. In the study, delivery time, energy consumption, and operating costs were calculated, and an analysis was conducted comparing them with those of a traditional ground transportation system.

System model

The drone-based blood transportation logistics system was modeled as an integrated system composed of the following main components:

• Source: The center where blood and blood products are prepared and stored (e.g., the National Center for Transfusion Medicine)

• Destination: Remote district hospitals

• Drone (UAV): Unmanned aerial vehicle performing the transport

• Control system: A system that ensures flight control, navigation, and safety

The primary objective of this system is to reduce delivery time, ensure reliable operation, and optimize energy consumption.

Mathematical model

A mathematical model based on the following key parameters was developed to evaluate the performance of a drone-based delivery system.

Delivery time: The total delivery time of the drone was defined as the combined duration of takeoff, flight, and landing:

Here,

T – total delivery time,

D – delivery distance,

V – average drone speed,

$t_{\mathrm{takeoff}}$ , $t_{\mathrm{landing}}$ – akeoff and landing times.

Energy consumption: The drone's energy consumption was determined by flight duration and payload weight.

Here:

E – total energy consumption,

P – drone capacity,

T – flight duration,

W – payload weight,

k – weight influence coefficient.

Cost function: The total operating cost is defined as follows:

Here, $C_f$ – is the fixed cost, $C_e$ – is the energy cost, $C_m$ – is the maintenance cost

Optimization objective: To improve system performance, a multi-objective optimization function was used to trade off and minimize time and cost.

Here, α and β are weighting coefficients that represent the relative importance of time and cost.

Constraints

1. Flight Range Limit. The total flight range of the drone must not exceed the technically achievable maximum limit:

Load Limit

The cargo weight loaded onto the drone does not exceed the device's load limit:

Energy Limit. The total energy consumed during the flight must not exceed the battery capacity:

Temperature Control Limits. To maintain the quality of blood and blood products, the following temperature conditions must be met during transportation:

Weather Limits. To ensure safe drone operations, the following restrictions were considered based on weather conditions:

These constraints form the basis for assessing the safety, energy efficiency, compliance with medical temperature standards, and feasibility of implementation of a drone-based delivery system under real-world conditions.

2.4 Technical specifications. The main technical specifications of the drone used in the study:

• Flight range: 20–40 km

• Payload: 2–5 kg

• Average speed: 60–80 km/h

• Temperature control: 2–6 °C

These specifications were evaluated, adapted to Mongolia's conditions, and used in the simulation model.

Research Results

We compared and evaluated the delivery times for transporting blood and blood products to hospitals in remote districts of Ulaanbaatar using traditional ground transport versus a drone-based system.

From Table 1, it can be observed that the drone-based delivery system is significantly faster than traditional ground transport. The reduction in delivery time was determined as follows:

Calculated using the mean values of the study: $R_T=\frac{\mathrm{45-15}}{45}\mathrm{\times 100=66.7}\%$ Thus, by using drones, delivery times have been reduced by an average of 66.7%, becoming approximately two to three times faster.

Figure 1 shows the difference in average delivery time between the traditional ground transportation system and the drone-based delivery system. The results indicate that using drones reduces delivery time by approximately 66.7%.

Based on real-world delivery data from the study, the overall performance of drone-based and traditional ground transportation systems was compared and evaluated. The results show that the total delivery time using drones was 6,540 minutes, while the total time for the traditional transportation system was 19,923 minutes.

The total delivery time savings were determined as follows:

These results show that the drone-based system reduces the total delivery time by approximately 3.05, achieving a 67.2% reduction in overall duration.

The cost of a single delivery operation was compared between traditional ground transportation and a drone-based system.

The average cost reduction was determined as follows:

According to the research results:

Thus, the use of drones has reduced the cost of a single delivery by an average of 64.6%.

Figure 2 compares delivery costs, showing that the drone-based system significantly reduces operating expenses compared to traditional ground transportation. The average cost reduction of 64.6% demonstrates that drone technology is highly cost-effective.

The delivery system's efficiency was evaluated by the number of deliveries performed per day. Assuming an 8-hour (480-minute) workday, the calculation was performed as follows based on the average duration of a single delivery.

For ground transportation:

For a drone-based system: $,P_{\mathrm{drone}}=\frac{T_{\mathrm{day}}}{T_{\mathrm{drone}}}=\frac{480}{15}\mathrm{\approx 32}\mathrm{delivery/day}$ Here $P$ – the number of deliveries per day, $T_{\mathrm{day}}$ – the total working time per day (minutes) $T_{\mathrm{ground}}$ , $T_{\mathrm{drone}}$ – is the average time per delivery.

The system's productivity increase was determined as follows:

The drone-based system increases the number of deliveries per day by approximately 3.2 times and significantly improves the system's overall productivity.

The drone's energy consumption was evaluated as a function of delivery distance. For the route selected in the study, the relationship between distance, delivery time, and energy consumption is shown in the following table.

The relationship between energy consumption (E) and distance (D) can be expressed as follows:

When the coefficient is estimated approximately from the research data:

Therefore, the average coefficient:

The results of the study show that the drone's energy consumption increases linearly with its flight distance. The energy consumption per unit distance is approximately 8.2 Wh/km, which is an important metric for flight planning and route optimization.

The route efficiency of the drone-based delivery system was evaluated against the characteristics of traditional ground transportation routes. In traditional transportation, factors such as road networks, intersections, and traffic congestion tend to increase both route length and travel time. However, drones can fly a direct path between two points, which shortens the route and reduces delivery time.

The efficiency of the route can be expressed as follows:

$D_{\mathrm{direct}}$ – is the drone's direct flight distance,

$D_{\mathrm{road}}$ – is the ground transport route length.

In general, since $D_{\mathrm{direct}}<D_{\mathrm{road}}$

The study's results were consolidated to compare and evaluate the performance of drone-based and traditional ground transportation systems using the following key indicators.

Table 4 shows that the drone-based delivery system outperforms traditional ground transport across all metrics. For example, delivery times have decreased by an average of 66.7%, and operating costs have been reduced by 64.6%. Additionally, the number of deliveries per day increased by 3.2 times, indicating a significant improvement in system efficiency.

Discussion

The research findings indicate that the drone-based delivery system outperforms the traditional ground transportation system in all key metrics. In particular, the 66.7% reduction in delivery time, the 64.6% decrease in costs, and the 3.2-fold increase in productivity clearly demonstrate the benefits of drone technology in logistics.

These results are consistent with international studies on drone-based logistics systems. Previous research has shown that using drones can shorten delivery times and optimize routes [3], [4], [5]. Researchers also highlight that drones can bypass traditional ground-transport delays and use direct routes [6].

Real-world applications are validating the effectiveness of drone technology. For example, a drone-based system implemented in Rwanda was found to significantly reduce blood delivery times, delivering faster than traditional transport [7]. Moreover, the use of drones has increased the efficiency of medical product delivery and improved access to services in remote areas [8]. Furthermore, studies have shown that transporting blood and blood products by drone does not adversely affect their quality [9].

In Mongolia, there are specific challenges to implementing a drone-based system. Ulaanbaatar's climate is harsh, with strong winds and cold temperatures that can negatively affect drone flight stability and battery performance [11]. Studies have shown that under low-temperature conditions, battery capacity decreases and flight duration is reduced [11], [12].

On the other hand, maintaining the quality of blood and blood products is one of the most critical requirements in medical logistics. International standards require that blood products be stored at a temperature between 2 and 6 °C, which demands precise engineering solutions for temperature control in drone-based systems [13]. Studies have shown that no statistically significant changes in the quality of blood transported by drones were observed, but that temperature stability is a critical factor [9].

A drone-based logistics system is not only technically but also strategically significant and can serve as a component of a smart city and a digital health system [14], [15]. Studies show that drones are becoming socially significant solutions by increasing access to health services and shortening emergency response times [14], [15].

This study has certain limitations. The model used in the research is simulation-based and may not fully capture all real-world factors. In particular, the lack of detailed consideration of factors such as dynamic weather changes, flight errors, and system delays could affect the results. Therefore, further real-world testing and research based on actual data are required.

Conclusion

This study evaluated a drone-based logistics system for delivering blood and blood products in Ulaanbaatar's remote districts and determined that it is feasible. According to the study's results, using drones can reduce delivery time by 2–3 times compared to traditional ground transport (66.7%) and cut operating costs by 64.6%. It also showed that the number of deliveries per day increased by 3.2 times, and that the system's overall efficiency and reliability improved significantly.

Therefore, a drone-based delivery system is a highly feasible, practically significant solution for increasing access to health services in the context of Mongolia, This study demonstrates that a drone-based delivery system is a highly feasible and practically significant solution for increasing the accessibility of health services and improving the efficiency of emergency aid delivery in the context of Mongolia.

Declarations

Funding: No funding was received

Competing interests: The authors declare no competing interests