Abstract
The use of Unmanned Aerial Vehicles (UAV) in situations where access is difficult due to challenging geographical conditions and destroyed infrastructure has emerged as a critical solution in disaster management and emergency response processes. This study addresses a Vehicle Routing Problem (VHR) to optimize the distribution of relief supplies via UAV following emergencies such as natural disasters. To solve this problem, k-means clustering was employed to group the delivery points, and the Nearest Neighbor (Nearest Neighbor – NN) algorithm was used to determine the shortest routes for each group. The study first introduces the basic constraints of the targeted VHR and then explains how the k-means and NN algorithms were integrated. The proposed algorithm is designed to operate under certain constraints, such as vehicle capacity and maximum distance traveled, and was tested using real-world data on emergency assembly points in the Gaziantep province. The experimental results indicate that relief packages can be delivered to 34 emergency assembly points in Gaziantep within less than 20 minutes using 6 UAV. This study demonstrates the effectiveness and efficiency of UAV usage in providing logistical support in challenging and urgent situations.
References
- Azmat, M., Kummer, S. 2020. Potential applications of unmanned ground and aerial vehicles to mitigate challenges of transport and logistics-related critical success factors in the humanitarian supply chain. Asian journal of sustainability and social responsibility, 5(1), 3. Doi: 10.1186/s41180-020-0033-7
- Bekhti, M., Abdennebi, M., Achir, N., Boussetta, K. 2016. Path planning of unmanned aerial vehicles with terrestrial wireless network tracking. 2016 Wireless Days (WD), pp 1-6, IEEE, Toulouse, France. Doi: 10.1109/WD.2016.7461521
- Bejiga, MB., Zeggada, A., Nouffidj, A., Melgani, F. 2017. A convolutional neural network approach for asissting avalanche search and rescue operations with UAV imagery. Remote Sensing, 9(2):100. Doi: 10.3390/rs9020100
- Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, SG., Bian, L. 2017. Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188:167-184. Doi: 10.1016/j.ijpe.2017.03.024
- Hachiya, D., Mas, E., Koshimura, S. 2022. A reinforcement learning model of multiple UAVs for transporting emergency relief supplies. Applied Sciences, 12(20):10427. Doi: 10.3390/app122010427
- Huang, Y., Han, H., Zhang, B., Su, X., Gong, Z. 2020. Supply distribution center planning in UAV-based logistics networks for post-disaster supply delivery. 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), pp. 1-6, Shenzhen, China. Doi: 10.1109/HEALTHCOM49281.2021.9399012
- Khan, SI., Qadir, Z., Munawar, HS., Nayak, SR., Budati, AK., Verma, KD., Prakash, D. 2021. UAVs path planning architecture for effective medical emergency response in future networks. Physical Communication, 47:101337. Doi: 10.1016/j.phycom.2021.101337
- Khoufi, I., Laoutiti, A., Adjih, C. 2019. A survey of recent extended variants of the traveling salesman and vehicle routing problems for unnamed aerial vehicles. Drones, 3(3):66. Doi: 10.3390/drones3030066
- Lee, J., Sung, CK., Oh, J., Han, K., Lee, S., Yu, MJ. 2020. A pragmatic approach to the design of advanced precision terrain-aided navigation for UAVs and its verification. Remote Sensing, 12(9):1396. Doi: 10.3390/rs12091396
- Mishra, B., Garg, D., Narang, P., Mishra, V. 2020. Drone- Surveillance for search and rescue in Natural Disaster. Computer Communications, 156:1-10. Doi: 10.1016/j.comcom.2020.03.012
- Nedjati, A., Vizvari, B., Izbirak, G. 2016. Post-earthquake response by small UAV helicopters. Natural Hazards, 80:1669-1688. Doi: 10.1007/s11069-015-2046-6
- Pakkan, B., Ermiş, M. 2010. İnsansız hava araçlarının genetik algoritma yöntemiyle çoklu hedeflere planlanması. Havacılık ve Uzay Teknolojileri Dergisi, 4(3):77-84.
- Polat, N. 2023. UAV-based investigation of earthquake-induced deformation and landscape changes: A case study of the February 6, 2023 Earthquakes in Hatay, Türkiye. Earth Science Informatics, 16(4):3765-3777. Doi: 10.1007/s12145-023-01128-y
- Praveen, V., Keerthika, P., Sivapriya, G., Sarankumar, A., Bhasker, B. 2022. Vehicle routing optimization problem: A study on capacitated vehicle routing problem. Materials Today: Proceedings, 64:670-674. Doi: 10.1016/j.matpr.2022.05.185
- Radzki, G., Golinska-Dawson, P., Bocewicz, G., Banaszak, Z. 2021. Modelling robust delivery scenarios for a fleet of unmanned aerial vehicles in disaster relief missions. Journal of Intelligent & Robotic Systems, 103:1-18. Doi: 10.1007/s10846-021-01502-2
- Restas, A. 2015. Drone applications for supporting disaster management. World Journal of Engineering and Technology, 3(3):316-321. Doi: 10.4236/wjet.2015.33C047
- Toth, P., Vigo, D. 2002. The vehicle routing problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 358 pp. Doi: 10.1137/1.9780898718515
- Toth, P., Vigo, D. 2014. Vehicle routing: Problems, methods, and applications. Society for industrial and applied mathematics, Philadelphia, PA, USA, 449 pp. Doi: 10.1137/1.9781611973594
- Wang, N., Christen, M., Hunt, M., Biller-Andorno, N. 2022. Supporting value sensitivity in the humanitarian use of drones through an ethics assessment framework. International Review of the Red Cross, 104(919):1397-1428. Doi: 10.1017/S1816383121000989
- Yandex 2024. https://yandex.com.tr/harita/103825/gaziantep
- Yang, Y., Qiu, X., Li, S., Wang, J., Chen, W., Hung, PCK., Zheng, Z. 2019. Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster. Chaos, 29(6):063106. Doi: 10.1063/1.5092740
- Zhao, N., Lu, W., Sheng, M., Chen, Y., Tang, J., Yu, FR., Wong, KK. 2019. UAV-Assisted emergency networks in disasters. IEEE Wireless Communications, 26(1):45-51. Doi: 10.1109/MWC.2018.1800160
- Zhang, Z., Wu, J., He, C. 2019. Search method of disaster inspection coordinated by multi-UAV. 2019 Chinese control conference (CCC), pp. 2144-2148, Guangzhou, China. Doi: 10.23919/ChiCC.2019.8865367
- Zhang, Y., Zhao, Q., Mao, P., Bai, Q., Li, F., Pavlova, S. 2024. Design and control of an ultra-low-cost logistic delivery fixed-wing UAV. Applied Sciences, 14(11):4358. Doi: 10.3390/app14114358
Abstract
Zorlu coğrafi koşullar ve yıkılmış altyapılar nedeniyle erişimin zor olduğu durumlarda İnsansız Hava Araçlarının (İHA) kullanımı, afet yönetimi ve acil müdahale süreçlerinde kritik bir çözüm olarak öne çıkmaktadır. Bu çalışmada, doğal afetler gibi acil durumlar sonrasında İHA’lar ile yardım malzemesi dağıtımını optimize etmek amacıyla bir Araç Rotalama Problemi (ARP) ele alınmıştır. Bu problemin çözümü için k-means kümeleme algoritması ile ziyaret noktalarını gruplara ayırma ve her bir grup için en yakın komşu (Nearest Neighbor – NN) algoritması ile en kısa rotaları belirleme yöntemleri kullanılmıştır. Çalışmada, öncelikle çalışılan ARP’nin temel kısıtları tanıtılmış, ardından, k-means ve NN algoritmalarının nasıl entegre edildiği açıklanmıştır. Önerilen algoritma, belirli kısıtlar altında (araç kapasitesi ve kat edilen maksimum mesafe gibi) çalışacak şekilde tasarlanmış ve gerçek dünya verileri kullanılarak Gaziantep ilindeki acil toplanma alanları üzerinde test edilmiştir. Elde edilen deneysel sonuçlar, Gaziantep ilindeki 34 acil toplanma alanına yardım paketlerinin 6 İHA ile 20 dakikadan az bir süre içinde ulaştırılabileceğini göstermektedir. Bu çalışma, İHA kullanımının zorlu ve acil durumlar için lojistik destek sağlama konusunda ne kadar etkili ve verimli olabileceğini göstermiştir.
Keywords
En yakın komşu algoritması insansız hava araçları k-means algoritması rotalama problemleri.
References
- Azmat, M., Kummer, S. 2020. Potential applications of unmanned ground and aerial vehicles to mitigate challenges of transport and logistics-related critical success factors in the humanitarian supply chain. Asian journal of sustainability and social responsibility, 5(1), 3. Doi: 10.1186/s41180-020-0033-7
- Bekhti, M., Abdennebi, M., Achir, N., Boussetta, K. 2016. Path planning of unmanned aerial vehicles with terrestrial wireless network tracking. 2016 Wireless Days (WD), pp 1-6, IEEE, Toulouse, France. Doi: 10.1109/WD.2016.7461521
- Bejiga, MB., Zeggada, A., Nouffidj, A., Melgani, F. 2017. A convolutional neural network approach for asissting avalanche search and rescue operations with UAV imagery. Remote Sensing, 9(2):100. Doi: 10.3390/rs9020100
- Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, SG., Bian, L. 2017. Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188:167-184. Doi: 10.1016/j.ijpe.2017.03.024
- Hachiya, D., Mas, E., Koshimura, S. 2022. A reinforcement learning model of multiple UAVs for transporting emergency relief supplies. Applied Sciences, 12(20):10427. Doi: 10.3390/app122010427
- Huang, Y., Han, H., Zhang, B., Su, X., Gong, Z. 2020. Supply distribution center planning in UAV-based logistics networks for post-disaster supply delivery. 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), pp. 1-6, Shenzhen, China. Doi: 10.1109/HEALTHCOM49281.2021.9399012
- Khan, SI., Qadir, Z., Munawar, HS., Nayak, SR., Budati, AK., Verma, KD., Prakash, D. 2021. UAVs path planning architecture for effective medical emergency response in future networks. Physical Communication, 47:101337. Doi: 10.1016/j.phycom.2021.101337
- Khoufi, I., Laoutiti, A., Adjih, C. 2019. A survey of recent extended variants of the traveling salesman and vehicle routing problems for unnamed aerial vehicles. Drones, 3(3):66. Doi: 10.3390/drones3030066
- Lee, J., Sung, CK., Oh, J., Han, K., Lee, S., Yu, MJ. 2020. A pragmatic approach to the design of advanced precision terrain-aided navigation for UAVs and its verification. Remote Sensing, 12(9):1396. Doi: 10.3390/rs12091396
- Mishra, B., Garg, D., Narang, P., Mishra, V. 2020. Drone- Surveillance for search and rescue in Natural Disaster. Computer Communications, 156:1-10. Doi: 10.1016/j.comcom.2020.03.012
- Nedjati, A., Vizvari, B., Izbirak, G. 2016. Post-earthquake response by small UAV helicopters. Natural Hazards, 80:1669-1688. Doi: 10.1007/s11069-015-2046-6
- Pakkan, B., Ermiş, M. 2010. İnsansız hava araçlarının genetik algoritma yöntemiyle çoklu hedeflere planlanması. Havacılık ve Uzay Teknolojileri Dergisi, 4(3):77-84.
- Polat, N. 2023. UAV-based investigation of earthquake-induced deformation and landscape changes: A case study of the February 6, 2023 Earthquakes in Hatay, Türkiye. Earth Science Informatics, 16(4):3765-3777. Doi: 10.1007/s12145-023-01128-y
- Praveen, V., Keerthika, P., Sivapriya, G., Sarankumar, A., Bhasker, B. 2022. Vehicle routing optimization problem: A study on capacitated vehicle routing problem. Materials Today: Proceedings, 64:670-674. Doi: 10.1016/j.matpr.2022.05.185
- Radzki, G., Golinska-Dawson, P., Bocewicz, G., Banaszak, Z. 2021. Modelling robust delivery scenarios for a fleet of unmanned aerial vehicles in disaster relief missions. Journal of Intelligent & Robotic Systems, 103:1-18. Doi: 10.1007/s10846-021-01502-2
- Restas, A. 2015. Drone applications for supporting disaster management. World Journal of Engineering and Technology, 3(3):316-321. Doi: 10.4236/wjet.2015.33C047
- Toth, P., Vigo, D. 2002. The vehicle routing problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 358 pp. Doi: 10.1137/1.9780898718515
- Toth, P., Vigo, D. 2014. Vehicle routing: Problems, methods, and applications. Society for industrial and applied mathematics, Philadelphia, PA, USA, 449 pp. Doi: 10.1137/1.9781611973594
- Wang, N., Christen, M., Hunt, M., Biller-Andorno, N. 2022. Supporting value sensitivity in the humanitarian use of drones through an ethics assessment framework. International Review of the Red Cross, 104(919):1397-1428. Doi: 10.1017/S1816383121000989
- Yandex 2024. https://yandex.com.tr/harita/103825/gaziantep
- Yang, Y., Qiu, X., Li, S., Wang, J., Chen, W., Hung, PCK., Zheng, Z. 2019. Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster. Chaos, 29(6):063106. Doi: 10.1063/1.5092740
- Zhao, N., Lu, W., Sheng, M., Chen, Y., Tang, J., Yu, FR., Wong, KK. 2019. UAV-Assisted emergency networks in disasters. IEEE Wireless Communications, 26(1):45-51. Doi: 10.1109/MWC.2018.1800160
- Zhang, Z., Wu, J., He, C. 2019. Search method of disaster inspection coordinated by multi-UAV. 2019 Chinese control conference (CCC), pp. 2144-2148, Guangzhou, China. Doi: 10.23919/ChiCC.2019.8865367
- Zhang, Y., Zhao, Q., Mao, P., Bai, Q., Li, F., Pavlova, S. 2024. Design and control of an ultra-low-cost logistic delivery fixed-wing UAV. Applied Sciences, 14(11):4358. Doi: 10.3390/app14114358
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | April 22, 2025 |
| Submission Date | June 13, 2024 |
| Acceptance Date | January 8, 2025 |
| Published in Issue | Year 2025 Volume: 15 Issue: 1 |
