## 1 Introduction
[19] analyzed several probabilistic scenarios to determine the most frequent emergency operation plan and the most persistent best compromised emergency operation plan. [PERSON] [20] constructed a resource scheduling model by considering influencing factors such as transportation time, cost and resource availability. Particle swarm optimization combined with Cuckoo search (PSO-CS) algorithm was applied to solve a resource scheduling process of multiple types and multiple classes. [PERSON] et al. [7] formulated the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and developed a solution framework to effectively deliver expendable and non-expendable resources in emergency recovery operations. [PERSON] and [PERSON] [21] established two optimal models, respectively; the single objective model aimed at minimizing the time of emergency resource dispatch, and the multi objective model was for the sake of minimizing the time of emergency resource dispatch and the number of emergency rescue bases, and a voting analytic hierarchy process was used to solve the model. [PERSON] and [PERSON] [22] constructed a capacitated vehicle routing problem model suitable for emergency resource scheduling process and designed a simulated annealing-genetic algorithm (SA-GA). Many of the models developed for emergency resources scheduling are deterministic and had the minimization of travel time as one of the objectives; they ignored the rescue routing problem. Moreover, heuristic methods are always used to find a near-optimal due to the NP-hard nature of the problem. For example, the PSO-CS algorithm was applied to solve the resource scheduling process of multiple types and multiple classes by [PERSON] [20]. An adaptively mutate genetic algorithm (GA) was used to solve an emergency resource scheduling model and included multiple suppliers with a variety of resources by [PERSON] et al. [17]. SA-GA was proposed in order to confront the capacitated vehicle routing problem for emergency resource scheduling process by [PERSON] and [PERSON] [22]. [PERSON] et al. [23] used GA to find out the dynamic emergency distribution path. [PERSON] et al. [24]and [PERSON] et al. [25] proposed a multi-agents approach by using a genetic algorithm for scheduling vehicle routing and local search for the management of an eventual event. Moreover, [PERSON] et al. [26] designed a quick heuristic algorithm in order to obtain a fleet dispatching plan. [PERSON] et al. [27] proposed an algorithm based on the branch-and-cut-and-price paradigm in order to deal with the generalized location and distribution problem. [PERSON] et al. [28] used the greedy algorithm to optimize the distribution path. [PERSON] et al. [29] proposed a two-stage shortest path algorithm, which is composed of K-paths algorithm and shuffled frog leaping algorithm, in order to solve the dynamic paths planning problem of emergency vehicles. [PERSON] and [PERSON] [30] designed a multi-level clustering algorithm for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. [PERSON] et al. [31] adopted the K-means clustering algorithm in order to obtain some of the local distribution centers and used the PSO algorithm to design the local optimal allocation routings of emergency relief vehicles. [PERSON] et al. [32] used the hierarchical ascending clustering approach in order to discover the reliable delivery routes. [PERSON] et al. [33] proposed the NSGA-II and multi-objective firefly algorithm in order to deal with the multiobjective vehicle routing model that was developed. [PERSON] et al. [34] proposed a generic hybrid heuristic framework to solve the vehicle routing problem after a natural disaster. All these related investigations have given us much inspiration when studying the rescue resource scheduling problem. For dangerous goods accidents of railways, the rescue resource scheduling should meet the following requirements:  
1.