## 2 Literature Review  
### Train-Set Rescheduling Problem  
There are two scenarios of TRP. The first scenario is to optimize the train-set circulation plan corresponding to the current timetable, to meet the demands of the new timetable. [PERSON] et al. [1] optimized the allocation of locomotives and vehicles and built a model to match the vehicle model with the line demand. They use CPLEX to solve it. The results show that the plan prepared by the algorithm can significantly improve the service quality in the peak period, compared with the manual scheduling plan. [PERSON] et al. [2] considered the indicators, such as railway operation cost, reliability and service quality when optimizing the railway rolling stock circulation problem. Their model can serve multiple lines and realize train combination and splitting. Their method has been applied to railway lines in the Netherlands. [PERSON] et al. [3] established a model by corresponding train units and a given set of timed trips for solving the train-unit assignment problem (TUAP). Moreover, they considered the number of seats required by passengers on each trip. [PERSON] et al. [4] designed a model including shunting operations, propagated delays and the need for human resources. Through this model, the synchronous arrangement of locomotive and vehicle allocation and vehicle path is realized. They used the heuristic based on Benders Decomposition to solve the model. [PERSON] et al. [5] studied a train-unit scheduling problem (TUSP), which is similar to the TUAP. Compared with TUAP, they took more real-life factors into consideration, which are crucial in railway operation. They proposed a customized branch-and-price solver and adapted an adaptive node selection method to facilitate the solving process. For the railway system that contains different types of trains, there are different maintenance requirements. Thus, in some application scenarios, the maintenance factors need to be considered when scheduling the train-set circulation plan. [PERSON] et al. [6] proposed a train timetable adjusting the approach for trains that require maintenance within three days. [PERSON] et al. [7] studied the efficient cycle of rolling stock. They established an integer multicommodity flow model to maximize the use of vehicles. They consider coupling trains in peak hours. [PERSON] et al. [8] put forward a method to adjust the number of trains at stations along the railway line by branch pricing algorithm. [PERSON] et al. [9] proposed a mixed integer programming model of balance train route mileage. This model considers the limitation of maintenance capacity. [PERSON] et al. [10] proposed a method to reschedule the train-set circulation plan based on a current timetable, so as to meet the demands of the new timetable. [PERSON] et al. [11] described the railway rolling stock allocation problem based on a set partitioning multi-commodity flow (SPMCF) problem. [PERSON] et al. [12] constructed the rolling stock plan by using a heuristic algorithm. For specific needs, they use a hill climbing heuristic to optimize the obtained plan, to maximize the carrying capacity with as few trains as possible. [PERSON] et al. [13] established a model to improve the efficient utilization of rolling stock. They considered the overhaul of the train. The case study shows that their method can improve train-set utilized efficiency and solution efficiency. [PERSON] et al. [14] compare the TCPP to the aircraft route problem (ARP) and the vehicle route problem (VRP) in logistics. They proposed a train-set circulation optimization model, an effective multiple population genetic algorithm (MPGA) is used to solve the problem. Aims to minimize the cost of rescheduling train-sets, [PERSON] et al. [15] described the TCPP as a traveling salesman problem (TSP) and then solved it by a column generation and Lagrangian relaxation heuristics. [PERSON] et al. [16] studied the train utilization of railway transportation hubs. They built a TCP model.