# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
###### Abstract  
To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling rules, operational patterns, and collaborative mechanisms between stand allocation and ground support vehicles, taking into account the coupling relationship between airport operational and support resources. The pre-allocation of stands is conducted under the constraints of limited support resources, and the results are used as inputs for ground support vehicle scheduling. This combined optimization of stands and vehicle resources enhances the overall resource efficiency. The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. Computational experiments conducted at Shenzhen Airport show some improvements: For the stand allocation model, the model incorporates ground support service constraints for tow tractors and driving distances for ferry buses, thereby avoiding potential service conflicts and resource wastage. Secondly, for the scheduling of vehicles, by analyzing the operational patterns and service characteristics of different vehicles, the model improved vehicle utilization efficiency by 37.5%, reduced travel distance by 20.4%, and decreased waiting times by 57.6%, compared to the first-come-first-served strategy currently employed at airports. a 2024 14 11407 1224  
to increase capacity, while the other involves improving resource utilization efficiency to enhance airport throughput [1], which is the focus of this study. Airport resources include operational resources and support resources; this study focuses on stands and various ground support vehicles. Stands can be categorized into contact stands and remote stands. At busy airports, contact stands, which connect to terminals via jet bridges and are equipped with comprehensive turnaround service facilities, are in high demand. The core goal in the stand allocation problem is to assign available terminal or apron positions to each flight, minimizing passenger inconvenience and maximizing airport operational efficiency [2]. How to complete multiple support services for flights within a limited timeframe is one of the more complex challenges airports face. Currently, most airports adopt a first-come-first-served (FCFS) manual scheduling approach for vehicle dispatching, which means the ground support vehicles that complete their tasks first are assigned to the corresponding flights based on the flight arrival time. However, due to the limited availability of vehicles and the inability of manual scheduling to account for overall dynamics, the efficiency of vehicle scheduling is relatively low. Figure 1 shows the stand occupancy distribution at Shenzhen Airport over a single day. It can be observed that, due to the presence of overnight flights, the number of occupied stands is highest between 11 PM and 6 AM the following day. C-type and E-type aircraft dominate in number, and during certain periods, the high volume of C-type flights necessitates the use of combined stands, thereby reducing the availability of E-type stands. From 10 AM to 9 PM, the overall stand usage exhibits a dynamic balance, characterized by a high turnover rate and frequent stand usage during this period. Stand allocation problem and ground support vehicle scheduling problem involves a complex interaction of multiple resources and stakeholders. The stand allocation results directly affect the scheduling of ground support vehicles, and there are coupling relationships between different vehicle types. The use of some vehicles like tow tractors may impose indirect constraints on stand allocation. Therefore, optimizing the stand allocation and ground support vehicle scheduling for turnaround flights is of significant practical importance.