# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 1 Introduction
For example, taking supply risk and demand uncertainty into account, [PERSON] [8] adopted an objective programming method to minimize the difference between two emergency service goals. [PERSON] et al. [9] proposed a framework combining cumulative prospect theory and evolutionary game theory to analyze the emergency strategy formulation model of rescuers. The framework integrated the bounded rationality of rescuers and the uncertainties of secondary disasters to select the optimal emergency logistics path. According to the demand uncertainty of allocating emergency facilities and decision-making services in different emergency response stages, [PERSON] et al. [10] put forward a corresponding stochastic programming model based on different post-disaster scenarios. It implemented the allocation of temporary disaster response facilities. Afterward, based on adequate information during decision-making, [PERSON] [11] developed a multi-objective location-allocation model for emergency supply and distribution. The model is more focused on the uncertainty of disaster location and the inaccuracy of damage degree information. In terms of mobile emergency routing considering uncertainty, some research works were concerned about the configuration, efficiency, and realization of mobile emergency facilities. For example, [PERSON] et al. [12] provided a comprehensive framework for mobile emergency facilities in the transportation network. They introduced the spatial and temporal models of the emergency transportation network and the interaction between the location of facilities and the emergency network. In recent years, scholars have only begun to explore the scheduling problem of mobile emergency routing. In order to seek a reasonable team size and position for emergency facilities, [PERSON] [13] designed a two-stage programming model and obtained a robust optimization solution for the emergency mobile facility fleet. Then, based on the characteristics of emergency uninterrupted, [PERSON] [14] proposed an emergency mobile facility routing model to maximize the emergency service demand by allocating mobile facilities reasonably. With the support of the Internet of Things, mobile emergency systems can capture emergency information changes in real time when dealing with various uncertain risks. Thus, the multiple vacations (MVs) policy, mobile edge computing [15], start-up threshold (ST) policy [16], and uncrewed aerial vehicles (UAVs) were emerged in mobile emergency facilities due to their mobility and autonomy. Obviously, the comprehensiveness of information enhances the capacity of emergency response. However, the uncertainty of emergency scenarios is still unavoidable, which stimulates the study of uncertainty. [PERSON] [17] put forward a hierarchical structure model with a combined genetic algorithm. The model solved the uncertain emergency location-routing problem with higher efficiency. [PERSON] [18] presented an uncertain multi-objective location-routing programming model. It is constructed for emergency response with consideration of travel time, emergency relief costs, and carbon dioxide emissions via uncertainty theory. [PERSON] [19] recognized that both randomness and uncertainty should be considered in the preparation stage of these disasters. He used stochastic optimization and robust optimization to deal with the randomness of the affected areas and the uncertainty of the disaster intensities. Aiming at improving the resilience of the distribution system and analyzing the influence of uncertainties on the post-disaster emergency recovery process, [PERSON] et al. [20] proposed a multi-time-step rolling optimization strategy based on model predictive control, which shortened the path for the repair and the total duration of recovery. After reviewing the papers published on emergency logistics management, [PERSON] [21] pointed out that it is critical to ensure that an effective and efficient emergency logistics management system is in place to meet any uncertainties.