Version 1
: Received: 1 August 2024 / Approved: 1 August 2024 / Online: 2 August 2024 (00:17:23 CEST)
How to cite:
Ghaffar, M. A.; Peng, L.; Adeel, M.; Chen, T. Research on Vehicle-UAV Integrated Routing Optimization Problem to Deliver Medical Supplies. Preprints2024, 2024080112. https://doi.org/10.20944/preprints202408.0112.v1
Ghaffar, M. A.; Peng, L.; Adeel, M.; Chen, T. Research on Vehicle-UAV Integrated Routing Optimization Problem to Deliver Medical Supplies. Preprints 2024, 2024080112. https://doi.org/10.20944/preprints202408.0112.v1
Ghaffar, M. A.; Peng, L.; Adeel, M.; Chen, T. Research on Vehicle-UAV Integrated Routing Optimization Problem to Deliver Medical Supplies. Preprints2024, 2024080112. https://doi.org/10.20944/preprints202408.0112.v1
APA Style
Ghaffar, M. A., Peng, L., Adeel, M., & Chen, T. (2024). Research on Vehicle-UAV Integrated Routing Optimization Problem to Deliver Medical Supplies. Preprints. https://doi.org/10.20944/preprints202408.0112.v1
Chicago/Turabian Style
Ghaffar, M. A., Muhammad Adeel and Ting Chen. 2024 "Research on Vehicle-UAV Integrated Routing Optimization Problem to Deliver Medical Supplies" Preprints. https://doi.org/10.20944/preprints202408.0112.v1
Abstract
In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach integrating vehicle and Unmanned Aerial Vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework that employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency towards early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach that successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
