Unmanned Aerial Vehicle Routing and Facility Location Selection for Healthcare Supply Chain Management: A Case Study in Türkiye

Abstract

Healthcare supply chain management is of great importance, particularly for hospitals. To manage medical supply chains quickly and effectively requires new and optimized logistics strategies. The use of an unmanned aerial vehicle (UAV) enables the swift delivery of medical supplies, overcoming obstacles like traffic, natural disasters, or infrastructure damage, and saving lives in critical situations. This study focuses on the distribution phase of emergency medical supply chains using electric UAVs, aiming to save lives during crises by establishing an efficient, technological, and sustainable transportation system in healthcare. The strategic locations of the charging stations, which can also serve as medical supply depots, and the optimal routes for the vehicles are integrated. Using Multi-Criteria Decision-Making (MCDM) techniques, specifically the AHP and TOPSIS methods, the optimal charging points for electric unmanned aerial vehicles are determined by considering various criteria. From these identified facilities, a multi-depot vehicle routing model was developed for supplying emergency medical supplies to hospitals, considering vehicle characteristics, hospital demand quantities, and facility capacities. This integrated methodology was applied to the province of Trabzon in Türkiye. Three different routes covering a total distance of 29.59 kilometres were generated to supply medical materials from five identified facilities to ten hospitals.

Keywords

• Medical supply chain management
• Vehicle Routing Problem
• AHP
• TOPSIS

References

1. Al-Rabiaah, S., Hosny, M., & AlMuhaideb, S. (2022). A Greedy Heuristic Based on Optimizing Battery Consumption and Routing Distance for Transporting Blood Using Unmanned Aerial Vehicles. Electronics (Switzerland), 11(20). https://doi.org/10.3390/electronics11203399 google scholar
2. Amirsahami, A., Barzinpour, F., & Pishvaee, S. (2023). A hierarchical model for strategic and operational planning in blood transportation with drones. PLoS ONE, 18(9 September). https://doi.org/10.1371/journal.pone.0291352 google scholar
3. Ayyildiz, E., & Taskin Gumus, A. (2021). Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transporta-tion: an application in Istanbul. Environmental Science and Pollution Research, 28(27). https://doi.org/10.1007/s11356-021-13223-y google scholar
4. BraBeL, H., Zeh, T., Fricke, H., & ELtner, A. (2023). OptimaL UAV Hangar Locations for Emergency Services Considering Restricted Areas. Drones, 7(3). https://doi.org/10.3390/drones7030203 google scholar
5. BruneLLi, M., Ditta, C. C., & Postorino, M. N. (2023). New infrastructures for Urban Air MobiLity systems: A systematic review on vertiport Location and capacity. Journal of Air Transport Management, 112. https://doi.org/10.1016/j.jairtraman.2023.102460 google scholar
6. Chen, Z. S., & Ruan, J. Q. (2024). Metaverse heaLthcare suppLy chain: ConceptuaL framework and barrier identification. Engineering Applications of Artificial Intelligence, 133. https://doi.org/10.1016/j.engappai.2024.108113 google scholar
7. Dixit, A., Routroy, S., & Dubey, S. K. (2019). A systematic Literature review of heaLthcare suppLy chain and impLications of future research. In International Journal of Pharmaceutical and Healthcare Marketing (VoL. 13, Issue 4). https://doi.org/10.1108/IJPHM-05-2018-0028 google scholar
8. EichLeay, M., Evens, E., Stankevitz, K., & Parker, C. (2017). Using the Unmanned Aerial Vehicle Delivery Decision Tool to Consider Trans-porting Medical Supplies via Drone. www.ghspjournaL.org google scholar

9. Escribano Macias, J., AngeLoudis, P., & Ochieng, W. (2020). OptimaL hub seLection for rapid medicaL deLiveries using unmanned aeriaL vehicLes. Transportation Research Part C: Emerging Technologies, 110. https://doi.org/10.1016/j.trc.2019.11.002 google scholar
10. FadhiL, D. N. (2018). A GIS-based AnaLysis for SeLecting Ground Infrastructure Locations for Urban Air MobiLity. Professorship for Modeling Spatial Mobility, May. google scholar
11. FathoLLahi, A., Derakhshandeh, S. Y., Ghiasian, A., & Khooban, M. H. (2022). UtiLization of dynamic wireLess power transfer technoLogy in muLti-depot, muLti-product deLivery suppLy chain. Sustainable Energy, Grids and Networks, 32. https://doi.org/10.1016/j.segan. 2022.100836 google scholar
12. Gao, J., & Zhen, L. (2024). InteLLigent Distribution Transportation Route PLanning Considering Traffic Congestion. IEEE Intelligent Trans-portation Systems Magazine, 16(3). https://doi.org/10.1109/MITS.2023.3306969 google scholar
13. Gupta, A., Borst, C., & Mulder, M. (2022). Human Performance in Solving Multi-UAV Over-Constrained Dynamic Vehicle Routing Problems. IFAC-PapersOnLine, 55(29). https://doi.org/10.1016/j.ifacol.2022.10.234 google scholar
14. Hwang, C.-L., & Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications. Multiple Attribute Decision Making. google scholar
15. Jeong, J., So, M., & Hwang, H. Y. (2021). Selection of vertiports using K-means algorithm and noise analyses for urban air mobility (UAM) in the Seoul metropolitan area. Applied Sciences (Switzerland), 11(12). https://doi.org/10.3390/app11125729 google scholar
16. Karabayir, A. N., Botsali, A. R., Kose, Y., & Cevikcan, E. (2020). Supplier selection in a construction company using fuzzy AHP and fuzzy TOPSIS. Advances in Intelligent Systems and Computing, 481-487. https://doi.org/l0.1007/978-3-030-23756-1_60 google scholar
17. Khan, S. I., Qadir, Z., Munawar, H. S., Nayak, S. R., Budati, A. K., Verma, K. D., & Prakash, D. (2021). UAVs path planning architecture for effective medical emergency response in future networks. Physical Communication, 47. https://doi.org/10.1016/j.phycom.2021. 101337 google scholar
18. Kim, M. S., Hong, W. H., Lee, Y. H., & Baek, S. C. (2022). Selection of Take-Off and Landing Sites for Firefighter Drones in Urban Areas Using a GIS-Based Multi-Criteria Model. Drones, 6(12). https://doi.org/10.3390/drones6120412 google scholar
19. Kouretas, K., & Kepaptsoglou, K. (2023). Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach. Vehicles, 5(3). https://doi.org/10.3390/vehicles5030058 google scholar
20. Kuo, R. J., Edbert, E., Zulvia, F. E., & Lu, S. H. (2023). Applying NSGA-II to vehicle routing problem with drones considering makespan and carbon emission. Expert Systems with Applications, 221. https://doi.org/10.1016/j.eswa.2023.119777 google scholar
21. Mitroudas, T., Balaska, V., Psomoulis, A., & Gasteratos, A. (2023). Multi-criteria Decision Making for Autonomous UAV Landing. IST 2023 -IEEE International Conference on Imaging Systems and Techniques, Proceedings. https://doi.org/10.1109/IST59124.2023.10355707 google scholar
22. Ozkan, O. (2023). Multi-objective optimization of transporting blood products by routing UAVs: the case of Istanbul. International Transactions in Operational Research, 30(1). https://doi.org/10.1111/itor.13109 google scholar
23. Ozkan, O., & Atli, O. (2021). Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: the case of Istanbul. Transportation Letters, 13(5-6). https://doi.org/10.1080/19427867.2021.1896063 google scholar
24. Patil, A., Shardeo, V., Dwivedi, A., Madaan, J., & Varma, N. (2021). Barriers to sustainability in humanitarian medical supply chains. Sustainable Production and Consumption, 27. https://doi.org/10.1016/j.spc.2021.04.022 google scholar
25. Saaty TL. 1980. The analytical hierarchy process. New York (NY): McGraw-Hill. google scholar
26. Saaty, T. L. (2001). Fundamentals of the Analytic Hierarchy Process. https://doi.org/10.1007/978-94-015-9799-9_2 google scholar
27. Shah Alam, M., & Oluoch, J. (2021). A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs). In Expert Systems with Applications (Vol. 179). https://doi.org/10.1016/j.eswa.2021.115091 google scholar
28. Sindhu, S., Nehra, V., & Luthra, S. (2017). Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. In Renewable and Sustainable Energy Reviews (Vol. 73). https://doi.org/10.1016/j.rser.2017.01.135 google scholar
29. StodoLa, P., & Kutej, L. (2024). MuLti-Depot VehicLe Routing Problem with Drones: MathematicaL formulation, solution algorithm and experiments. Expert Systems with Applications, 241. https://doi.org/10.1016/j.eswa.2023.122483 google scholar
30. Tyagi, M., Kumar, P., & Kumar, D. (2014). A hybrid approach using AHP-TOPSIS for anaLyzing e-SCM performance. Procedia Engineering, 97. https://doi.org/10.1016/j.proeng.2014.12.463 google scholar
31. Venkatesh, N., Payan, A. P., Justin, C. Y., Kee, E., & Mavris, D. N. (2020). OptimaL siting of sub-urban air mobiLity (Suam) ground architectures using network flow formuLation. AIAA AVIATION 2020 FORUM, 1 PartF. https://doi.org/10.2514/6.2020-2921 google scholar
32. Wang, J., Yuan, L., Zhang, Z., Gao, S., Sun, Y., & Zhou, Y. (2021). MuLtiobjective MuLtipLe Neighborhood Search ALgorithms for MuLtiobjective FLeet Size and Mix Location-Routing ProbLem with Time Windows. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4). https://doi.org/10.1109/TSMC.2019.2912194 google scholar
33. Wang, Y., Li, J., Yuan, Y., & Lai, C. S. (2024). Optimizing Urban Air MobiLity: A Ground-Connected Approach to SeLect OptimaL eVTOL Takeoff and Landing Sites for Short-Distance Intercity TraveL. IEEE Open Journal of Vehicular Technology. https://doi.org/10.1109/OJVT. 2024.3506277 google scholar
34. Wen, T., Zhang, Z., & Wong, K. K. L. (2016). MuLti-objective aLgorithm for bLood suppLy via unmanned aeriaL vehicLes to the wounded in an emergency situation. PLoS ONE, 11(5). https://doi.org/10.1371/journaL.pone.0155176 google scholar
35. Young Jeong, H., & Lee, S. (2023). Drone routing probLem with truck: Optimization and quantitative anaLysis. Expert Systems with Applications, 227. https://doi.org/10.1016/j.eswa.2023.120260 google scholar
36. Zabinsky, Z. B., DuLyakupt, P., Zangeneh-Khamooshi, S., Xiao, C., Zhang, P., KiatsupaibuL, S., & Heim, J. A. (2020). OptimaL coLLection of medicaL specimens and deLivery to centraL Laboratory. Annals of Operations Research, 287(1). https://doi.org/10.1007/s10479-019-03260-9 google scholar
37. Zhang, C., Zhao, Y., & Leng, L. (2020). A hyper-heuristic aLgorithm for time-dependent green Location routing probLem with time windows. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2991411 google scholar
38. Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets. International Journal of Intelligent Systems, 29(12), 1061-1078. https://doi.org/l0.1002/INT.21676 google scholar
39. Zheng, F., Du, L., Li, X., Zhang, J., Tian, B., & Jallad, R. (2023). Multi-objective medical supplies distribution open vehicle routing problem with fairness and timeliness under major public health emergencies. Management System Engineering, 2(1). https://doi.org/10. 1007/s44176-023-00015-6 google scholar