## 8 Conclusions  
A new routing problem based on a multivehicle point-to-point network inspired by the characteristics of the TSP and multidepot routing problem was proposed for application in complex UAM operation management systems by introducing new constraints to find and select the optimum route from one vertiport location to another. Because UAM operations require flight scheduling for the departure and arrival of each UAM, a mission library was created based on the corridor way points, mission speeds and UAM performance results. The mission library was integrated into our new formulation, in addition to the waiting time at each vertiport and the arrival and departure time variables, to allow for dynamic scheduling based on the mission flight time and the selected optimum routes. Our formulation for the point-to-point network problem was studied for different problem sizes; although it satisfied the problem definition for a point-to-point network, the computation time increased exponentially as the problem size increased, as the MILP algorithm needed to find the optimum routes for each vehicle. We therefore recommend that a new kind of method be developed that focuses on efficiency in terms of computation time. The study results show that our formulation for a point-to-point network with dynamic scheduling for UAM operation management satisfied the constraints with respect to waiting time, speed and routing selection, in addition to achieving an optimum flight schedule with the most suitable corridors for travelling from one location to another.  
Our formulation is subject to some limitations; for example, the customer requirements for travel from one location to another are not considered in our selection of route constraints. We suggest that our formulation be integrated by introducing a customer priority level for selection of different routes and setting vertiport capacities to limit the number of UAMs at vertiports. On the other hand, mission selection is used to achieve a dynamic AADS. In the future, a powerful algorithm such as machine learning could be integrated to select the mission, create a more advanced method and increase efficiency in terms of computation time.  
**Author Contributions:** Conceptualization, Z.W.T. and A.A.M.; Methodology, Z.W.T.; Validation, Z.W.T.; Investigation, Z.W.T.; Resources, D.K., J.L., W.-J.W., H.J.L. and N.L.Y.; Writing--original draft, Z.W.T.; Writing--review & editing, Z.W.T. and A.A.M.; Supervision, A.A.M. and J.-W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) via grants funded by the Ministry of Land, Infrastructure and Transport (Grant No. 22 ACTO-B151661-04 and Grant No. RS-2022-00143965) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2020R1A6A1A03046811).  
**Institutional Review Board Statement:** Not Available.  
**Informed Consent Statement:** Not Available.  
**Data Availability Statement:** The data used to support the findings of this study are available from the authors upon request.  
**Acknowledgments:** The authors would like to thank the editors and the reviewers for their comments on an earlier draft of this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.