# Research on Railway Emergency Resources Scheduling Model under Multiple Uncertainties
## 1 Introduction
### Literature Review  
Domestic and foreign scholars have researched such issues from different perspectives. For the study of the railway emergency management system, [PERSON] et al. presented a hierarchical timed Petri net (HTPN)-based method for creating visualized, formal, and digital railway emergency plans, improving their interpretability [1]. [PERSON] et al. proposed a scenario-response method for railway emergency management, utilizing dynamic Bayesian networks, fuzzy neural networks, and convolutional neural networks to deduce emergency states and their severity [2]. [PERSON] et al. introduced a space-time accessibility assessment method for railway emergency networks, utilizing the gravity model to enhance the traditional travel time budget model, ultimately providing a foundation for effective maintenance allocation strategies [3]. While a well-developed railway emergency plan inadvance plays an important role in reducing casualties and property damage, resource scheduling is the key to achieving an emergency plan. In terms of emergency resource scheduling research for emergencies, [PERSON] et al. proposed a location-routing problem (LRP) optimization model for emergency resource distribution after disasters, taking into account the interplay between distribution center location and routing schemes [4]. Based on the characteristics and requirements of emergency resource scheduling, [PERSON] et al. established a single-objective model with the shortest emergency resource scheduling time while constructing a dual-objective model with the shortest scheduling time and the fewest emergency rescue points [5], and then used the stratified sequencing method to solve it. [PERSON] et al. proposed a dual-objective resource scheduling method to optimize the resource transportation paths and reduce transportation costs [6]. In addition, based on the prospect theory, a fuzzy evaluation method is proposed to optimize the repair work. [PERSON] et al. integrated cloud computing and big data technology to study the resource scheduling problem of high-speed rail emergency services and used the virtualization of high-speed rail emergency resources as a service pool to establish a global optimal scheduling model for high-speed rail emergency resource service pools [7]. [PERSON] et al. established a multi-objective programming model with the goal of maximizing the satisfaction degree of scheduling time and the satisfaction degree related to the pairing of relief points and emergency points, while ensuring the maximum satisfaction degree of all emergency points [8]. [PERSON] et al. processed the road network through the network abstraction method under the premise of determining the allocation point of rescue resources [9]. Then, with the goal of minimizing the emergency response time and maximizing the resource requirements of accident spots, an emergency resource scheduling model based on phase coordination was established. [PERSON] et al. proposed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on gray interval numbers to solve the uncertainty problem of the number of emergency resources required at each disaster site [10]. [PERSON] et al. established a multi-objective mixed-integer linear programming (MILP) model with the least scheduling time and cost as the objective function, which considered the effectiveness of multiple supply sites, multiple disaster sites, multiple transportation models, uncertain demand and scheduling paths [11]. In addition, they used the augmented \(\varepsilon\)-constraint method (AUGMECON) combined with the robust optimization method to solve the model and reach the optimal scheduling scheme. For the research of uncertain information in resource scheduling, conventional processing methods include stochastic mathematical programming, fuzzy mathematical programming, and interval parameter programming. As for fuzzy mathematical programming, fuzzy set theory is usually used to deal with fuzzy uncertainty in the optimization model.