<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, the frequency of disasters, natural disasters, and other emergencies has been increasing worldwide. When an emergency occurs, effective rescue measures must be taken promptly to minimize the loss of life and property. In the process of rescuing casualties, a large amount of medical emergency supplies are urgently needed. Therefore, it is of great practical significance to study the vehicle path problem in medical emergency supplies dispatching. In this paper, we take the vehicle path optimization problem of medical emergency supplies dispatching considering the demand urgency as the research object, design the improved cuckoo-ant colony hybrid algorithm to solve the model based on the urgency analysis, and compare it with the ant colony algorithm and cuckoo algorithm to verify the efficiency of the designed algorithm. The results show that compared with the vehicle path scheme without considering the demand urgency, the path optimization scheme considering the demand urgency is more expensive and requires a small increase in time, but improves the efficiency and rationality of medical emergency supplies dispatching. The study of the emergency vehicle path problem can improve the weaknesses in the current emergency rescue decision-making, so that the emergency rescue work can be done quickly, economically, and reasonably, and provide a theoretical basis and suggestions for the emergency management department when making decisions.</p>
</abstract>

In recent years, the frequency of disasters, natural disasters, and other emergencies has been increasing worldwide. When an emergency occurs, effective rescue measures must be taken promptly to minimize the loss of life and property. In the process of rescuing casualties, a large amount of medical emergency supplies are urgently needed. Therefore, it is of great practical significance to study the vehicle path problem in medical emergency supplies dispatching. In this paper, we take the vehicle path optimization problem of medical emergency supplies dispatching considering the demand urgency as the research object, design the improved cuckoo-ant colony hybrid algorithm to solve the model based on the urgency analysis, and compare it with the ant colony algorithm and cuckoo algorithm to verify the efficiency of the designed algorithm. The results show that compared with the vehicle path scheme without considering the demand urgency, the path optimization scheme considering the demand urgency is more expensive and requires a small increase in time, but improves the efficiency and rationality of medical emergency supplies dispatching. The study of the emergency vehicle path problem can improve the weaknesses in the current emergency rescue decision-making, so that the emergency rescue work can be done quickly, economically, and reasonably, and provide a theoretical basis and suggestions for the emergency management department when making decisions.

Medical emergency supplies dispatching vehicle path optimization based on demand urgency

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In recent years, the frequency of disasters, natural disasters, and other emergencies has been increasing worldwide. When an emergency occurs, effective rescue...

Medical emergency supplies dispatching vehicle path optimization based on demand urgency

Online veröffentlicht: 22. Mai 2023

Seitenbereich: -

Eingereicht: 14. Juni 2022

Akzeptiert: 15. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00270

Schlüsselwörter
Tightness analysis, Vehicle scheduling, Path optimization, Cuckoo algorithm, Ant colony algorithm

© 2023 Min Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Medical emergency supplies dispatching vehicle path optimization based on demand urgency

Online veröffentlicht: 22. Mai 2023

Seitenbereich: -

Eingereicht: 14. Juni 2022

Akzeptiert: 15. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00270

SchlüsselwörterTightness analysis, Vehicle scheduling, Path optimization, Cuckoo algorithm, Ant colony algorithm

© 2023 Min Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Tightness analysis, Vehicle scheduling, Path optimization, Cuckoo algorithm, Ant colony algorithm