# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 7 Conclusions  
This study proposes a two-stage optimization model for scheduling airport operational and support resources. In addressing the stand allocation problem, we have incorporated considerations for the use of tractors and ferry vehicles, optimizing stand allocation results and pushback times, which serve as input for the second stage of support resource scheduling optimization. For the scheduling of support vehicles, we optimized task assignments and driving routes based on the collaborative relationships among vehicles. By addressing the stand allocation problem with regard to transient resources, we can mitigate delays caused by service conflicts among vehicles, while the optimized scheduling of support vehicles based on stand allocation results can enhance the utilization efficiency of existing airport support resources. The model we proposed is based on flight schedules to generate reasonable gate allocation plans and vehicle scheduling solutions, achieving airport resource scheduling and allocation during the pre-tactical phase. The research findings offer meaningful insights for the transient support of flights, improving the efficiency, safety, and effectiveness of airport ground operations, and effectively integrating existing resources. It can be regarded as a powerful decision-support tool for air traffic controllers and airport ground staff.  
Our proposed two-stage model assumes fixed driving routes and does not account for potential route conflicts among aircraft, vehicles, and between aircraft and vehicles. In future work, we will focus on further investigating the taxiing trajectories at the airport and the integrated scheduling and management of airport ground operations. Additionally, in scheduling vehicles, we did not consider personnel allocation. Future research could explore \"human-aircraft-vehicle\" multi-agent collaborative scheduling to promote the development of smart airports. To more comprehensively account for the actual operation of airports, future research could include different operational scenarios caused by weather conditions and emergencies, enabling a deeper exploration of the algorithm's performance under various scenarios. With the continuous development of artificial intelligence and machine learning technologies, future research can leverage these technologies to simulate and predict airport operation scenarios. These approaches can make the data more aligned with the actual conditions of airport operations, thereby improving the optimization of resource allocation methods.  
Figure 12: Test results for different minimum turnaround time of C-type aircraft. (**a**) Test results of taxi fuel consumption; (**b**) Test results of driving time of ferry vehicles; (**c**) Test results of idle times.  
**Author Contributions:** Conceptualization, M.Y. ([PERSON]) and J.Y.; Data curation, J.Y.; Formal analysis, J.S.; Funding acquisition, M.H. and J.Y.; Investigation, M.Y. ([PERSON]); Methodology, M.H.; Project administration, M.H.; Resources, M.H. and J.Y.; Software, M.Y. ([PERSON]) and J.S.; Supervision, M.H. and J.Y.; Validation, J.S. and M.Y. ([PERSON]); Visualization, M.Y. ([PERSON]) and J.S.; Writing--original draft, M.Y. ([PERSON]) and J.S.; Writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 52002178) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20190416).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.