REFERENCES
1. Sengupta, R.; Bulusu, V.; Mballo, C. E.; Onat, E. B.; Cao, S. Urban air mobility research: challenges and opportunities. Annu. Rev. Control. Robot. Auton. Syst. 2025, 8, 407-31.
2. Shavarani, S. M.; Nejad, M. G.; Rismanchian, F.; Izbirak, G. Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon Prime Air in the city of San Francisco. Int. J. Adv. Manuf. Technol. 2017, 95, 3141-53.
3. Kim, J.; Atkins, E. Airspace geofencing and flight planning for low-altitude, urban, small unmanned aircraft systems. Appl. Sci. 2022, 12, 576.
4. Lu, X.; Wu, Q.; Zhou, K. Path optimization for UAV food delivery under extreme weather conditions with wind disturbance consideration. Comput. Ind. Eng. 2025, 209, 111452.
5. Zhang, S.; Zeng, Y.; Zhang, R. Cellular-enabled UAV communication: a connectivity-constrained trajectory optimization perspective. IEEE. Trans. Commun. 2019, 67, 2580-604.
6. Bruneau, M.; Chang, S. E.; Eguchi, R. T.; et al. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra. 2003, 19, 733-52.
7. Henry, D.; Emmanuel Ramirez-Marquez, J. Generic metrics and quantitative approaches for system resilience as a function of time. Reliab. Eng. Syst. Safe. 2012, 99, 114-22.
8. Faturechi, R.; Miller-Hooks, E. Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review. J. Infrastruct. Syst. 2015, 21, 04014025.
9. Bergantino, A. S.; Gardelli, A.; Rotaris, L. Assessing transport network resilience: empirical insights from real-world data studies. Transp. Rev. 2024, 44, 834-57.
10. Xu, B.; Liu, T.; Bai, G.; Tao, J.; Zhang, Y.; Fang, Y. A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity. Reliab. Eng. Syst. Safe. 2022, 219, 108221.
11. Aggarwal, S.; Kumar, N. Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges. Comput. Commun. 2020, 149, 270-99.
12. Chung, S. H.; Sah, B.; Lee, J. Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. Comput. Oper. Res. 2020, 123, 105004.
13. Li, A.; Hansen, M.; Zou, B. Traffic management and resource allocation for UAV-based parcel delivery in low-altitude urban space. Transp. Res. Part. C. Emerg. Technol. 2022, 143, 103808.
14. Raivi, A. M.; Huda, S. M. A.; Alam, M. M.; Moh, S. Drone routing for drone-based delivery systems: A review of trajectory planning, charging, and security. Sensors 2023, 23, 1463.
15. Li, X.; Tupayachi, J.; Sharmin, A.; Martinez Ferguson, M. Drone-aided delivery methods, challenge, and the future: a methodological review. Drones 2023, 7, 191.
16. Betti Sorbelli, F. UAV-based delivery systems: a systematic review, current trends, and research challenges. ACM. J. Auton. Transport. Syst. 2024, 1, 1-40.
17. Guo, J.; Zhou, Y.; Burlion, L.; Savkin, A. V.; Huang, C. Autonomous UAV last-mile delivery in urban environments: a survey on deep learning and reinforcement learning solutions. Control. Eng. Pract. 2025, 165, 106491.
18. Chen, X.; Wang, H.; Li, Z.; et al. DeliverSense: efficient delivery drone scheduling for crowdsensing with deep reinforcement learning. In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers Adjunct UbiComp/ISWC '22 Adjunct; 2022, pp. 403-8.
19. Wu, G.; Fan, M.; Shi, J.; Feng, Y. Reinforcement learning based truck-and-drone coordinated delivery. IEEE. Trans. Artif. Intell. 2023, 4, 754-63.
20. Bi, Z.; Guo, X.; Wang, J.; Qin, S.; Liu, G. Deep reinforcement learning for truck-drone delivery problem. Drones 2023, 7, 445.
21. Wang, F.; Zhang, H.; Du, S.; Hua, M.; Zhong, G. C-SPPO: a deep reinforcement learning framework for large-scale dynamic logistics UAV routing problem. Chin. J. Aeronaut. 2025, 38, 103229.
22. Hu, Y.; Du, Y.; Li, S. Real-time coordination of human couriers and drones for on-demand food-delivery platforms: A multi-stage risk-aware multi-agent reinforcement learning framework. Transp. Res. Part. C. Emerg. Technol. 2025, 181, 105381.
23. Peng, W.; Wang, D.; Yin, Y.; Cheng, T. Multi-agent deep reinforcement learning-based truck–drone collaborative routing with dynamic emergency response. Transp. Res. Part. E. Logist. Transp. Rev. 2025, 195, 103974.
24. Rumman, K. M.; Kaltsogiannis, D.; Eleni Tsiropoulou, E. Intelligent drone pickup scheduling via deep reinforcement learning in low altitude economy networks. Internet. Things. 2026, 36, 101882.
25. Echard, B.; Gayton, N.; Lemaire, M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation. Struct. Saf. 2011, 33, 145-54.
26. Rasmussen, C. E.; Williams, C. K. I. Gaussian processes for machine learning. MIT Press; 2006. Available from: https://gaussianprocess.org/gpml/ [Last accessed on 30 Jun 2026].
27. Wan, L.; Wei, Y.; Zhang, Q.; Liu, L.; Chen, Y. A new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis. Reliab. Eng. Syst. Safe. 2025, 260, 110966.
28. Cimellaro, G. P.; Reinhorn, A. M.; Bruneau, M. Framework for analytical quantification of disaster resilience. Eng. Struct. 2010, 32, 3639-49.
29. Ouyang, M.; Dueñas-Osorio, L.; Min, X. A three-stage resilience analysis framework for urban infrastructure systems. Struct. Saf. 2012, 36-7, 23-31.
30. Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 2002, 145, 280-97.
