## 2 Literature Review
[21] implemented a trade-off between a track occupation time and the total walking distance to solve the track allocation problem. [PERSON] and [PERSON] [26] focused on the stop plan and ticket allocation problems that were significantly affected by passenger satisfaction and average seat occupancy rate. In addition, [PERSON] et al. [29] focused on the railway timetable rescheduling problem in cases of large disruptions from a macroscopic point of view. Based on population coverage, budget constraints, and origin-destination flows, [PERSON] et al. [30] proposed a model for expanding transportation networks from a macro perspective. However, the high-speed railway system is a multi-factor, multi-objective, and multi-function stochastic dynamic system. Therefore, in recent years, a few studies on optimizing high-speed railway problems have considered multi-objective models [8; 15]. [PERSON] et al. [14] proposed a multi-objective optimization model for train routing on a high-speed railway network, thereby providing an important reference for train planning to provide better services. [PERSON] et al. [31] described the train scheduling problem as a dynamic multi-objective optimization model that utilizes accurate results provided by detailed real-time train simulations. [PERSON] et al. [32] described a multi-criteria decision analysis approach that identified the best alternative from a given set of possible alternatives. Based on the Beijing-Shanghai high-speed railway and the existing railway network, [PERSON] et al. [33] established a multi-objective model for high-speed and medium-speed trains. At present, the model has been applied to the train diagram system of the Beijing-Shanghai high-speed railway. Nevertheless, the above studies only described the high-speed railway as a multi-objective model but did not optimize it. As shown in the above review, existing models and tools are mainly concerned with high-speed railway management. Most of them use a single objective to compute the location of high-speed railway systems, and their output is an analysis of different indexes that reflect the characteristics of the systems. Some scholars have focused on optimizing the high-speed railway strictly according to the given operation management, which means that robustness characteristics are seldom considered. However, various disturbances can influence real-world high-speed railway operations. Therefore, if we use these results to evaluate high-speed railway operation management, we will obtain incorrect outcomes because of the neglected disturbances. To address the above issues, this paper describes the high-speed railway line problem as a dynamic multi-objective model and carries out optimization research to provide accurate results. Rather than just focusing on a single perspective, this paper proposes a more realistic model that takes into account multiple perspectives: macro, meso, and micro. An optimization model is used to solve the dimension problems of the different objective functions using the economic transformation coefficient, and the best operation scheme for the high-speed railway is obtained. It is well known that there are many methods for solving optimization models. For highly complex and large-scale optimization models, an optimization algorithm is often used, such as the neural network algorithm [34; 35; 36], tabu search algorithm [22], genetic algorithm [9; 14; 18; 25; 36; 37], or simulated annealing algorithm [11; 21; 24]. However, the process of improving the applicability of the algorithm to multi-objective optimization problems remains to be further studied. In this paper, a multi-objective optimization model of the high-speed railway system is constructed by comprehensively considering the micro-, meso-, and macro-objectives. The Pareto optimization algorithms can offer a means to find a set of Pareto-optimal solutions in a single run [10; 38; 39].