## 2 Literature Review  
At present, many studies have been carried out at home and abroad in the field of airport surface operation optimization, mainly focusing on airport surface traffic operation modeling, airport surface performance index analysis, and airport surface resource optimization scheduling. In terms of airport surface traffic operation modeling, most recent studies have extracted the traffic characteristics of the airport surface and adopted the theory of Cellular Automata (CA): elements such as taxiways, aprons, and runways are considered as cellular space, and a cellular transport model of the traffic flow on the surface is established for simulation and deduction in order to clarify the spatial and temporal evolution law of the traffic flow on the surface ([PERSON], et al., 2018; [PERSON], et al., 2020; [PERSON], et al., 2016)[6; 7; 8]. In some studies, a directed topological network model was used in establishing the operational environment of the airport surface, the neighborhood relationships of the structural elements of the airport surface were described using \"nodes-segments\", and the static properties and dynamic response behavior of the airport surface traffic system were studied based on Petri net modeling ([PERSON], et al., 2021; [PERSON], et al., 2018)[9; 10]. Mathematical models based on queuing theory for aircraft runway departure queuing and ground protection have been established to achieve the prediction and optimization of key time nodes for aircraft surface operations ([PERSON], et al., 2019; [PERSON], et al., 2016)[11; 12]. [PERSON] considered the macro- and micro-effects of aircraft ground traffic flow arrival time separation distribution on airport operations and constructed a slowing probability model [13]. [PERSON] treated a single aircraft as an agent and developed a simulation model of airport surface operations based on a multiple-agent system [14]. In terms of analysis of airport surface performance indices, most studies focus on the performance domains of safety, capacity, and efficiency. [PERSON] used a weighted linear objective function to balance multiple types of conflict indices [15]. According to [PERSON] and [PERSON], multi-scenario-oriented surface capacity assessment and prediction models were developed considering the influence of meteorology, traffic, and airspace on the surface capacity, respectively [16; 17]. [PERSON] extracted taxing durations and flight flows at different levels of airport surface congestion and studied the effect of congestion on the aircraft taxiing process [18]. [PERSON] and [PERSON] analyzed the interactions between airport surface operation indices and assigned weights to the indices using hierarchical analysis and a topologically integrated evaluation method [19; 20]. In addition, a method of modeling and analysis of situational indices based on the macro-distribution network of the airport surface was constructed from the multidimensional perspective of surface traffic flow, take-off, landing queue length, and time slot resource demand, providing a theoretical basis for accurate perception of the field operating environment ([PERSON], et al., 2018; [PERSON], et al., 2021)[21; 22]. In terms of optimal scheduling of airport surface resources, the research focuses on parking stand allocation, taxiing planning, and runway scheduling, etc. In [PERSON] and [PERSON]'s studies, the arrival aircraft parking stand usage optimization and the departing aircraft push-back control optimization methods were respectively studied for the parking stand resource usage problem [23; 24]. Taxiway resource usage problems were investigated by [PERSON] and [PERSON] for taxiway schedules planning and taxiway path planning to ensure the safety and efficiency of aircraft operations [25; 26].