## 2 Literature Review
### EV Scheduling Problem  
According to the number of emergencies (or disaster sites) to be responded to, this can be categorized into single emergency response scheduling (SER) [9] and concurrent emergency response scheduling (CER) [10; 11]. Although both have the same optimization objective, scheduling decisions for CER are more difficult due to its large accident coverage and high losses [12]. In the early days, a deterministic strategy was often used in rescue response, where the nearest EVs were dispatched according to the first-in-first-out rule [13]. This strategy has two significant advantages: (a) it is easy to implement, and (b) it ensures the fastest response to rescue calls. However, this static scheduling strategy does not necessarily remain applicable, as related research gradually shifts to concurrent responses from multiple resource sites to multiple disaster sites. [PERSON] et al. [14] proved in 1972 that this scheduling strategy does not result in an optimal mean response time. [PERSON] [15] also used an approximate dynamic programming model to show that a more flexible scheduling and repositioning strategy reduces the mean response time by 12.89%. Therefore, the application of mathematical models to optimize the emergency response process has become mainstream nowadays [16]. [PERSON] et al. [17] proposed an optimization model based on a mixed-integer linear programming (MILP) formulation and a risk-based traffic optimization formulation to determine the optimal emergency dispatch routes using a multi-stage optimization algorithm. Their approach aims to enhance the efficiency of emergency traffic and coordinate the traffic flow of the road network during rescue and evacuation processes. [PERSON] et al. [18] proposed an emergency response model based on the integration of GIS and the MILP model, providing an emergency resource scheduling solution to minimize the weighted emergency response completion time.  
In contrast to the SER situation, the CER situation faces multiple disaster sites competing for resources, making it particularly important to manage the relationships between the disaster sites fairly and reasonably [19]. On the one hand, some scholars focus on how to distribute supplies equitably, as the discrepancy between the supply and demand of emergency resources in an emergency presents a significant challenge to emergency rescue work. [PERSON] et al. [20] studied the scheduling and allocation of emergency resources with the COVID-19 pandemic as the research background. Based on the degree of urgency, they constructed a multi-objective optimization model to maximize the satisfaction of disaster victims, minimize the total cost, and ensure the equitable distribution of supplies. On the other hand, achieving differentiated scheduling by prioritizing each disaster site is also a common approach. [PERSON] et al. [21] studied the issue of EV scheduling for forest fires. They addressed the varying demands of different fire sites and the competition for resources among them by establishing rescue priorities. Their approach aimed to minimize the overall firefighting time and the number of fire engines used. [PERSON] et al. [22] devised a relative deprivation cost metric to determine the rescue priority of each disaster site and optimized the rescue routes of EVs to achieve humanitarian rescue.