# Robust Optimization of Transport Organization for China Railway Express
###### Abstract
[PERSON] [5] examined Baoshen Railway's transportation organization optimization using qualitative measures and an evaluation system developed through the Delphi method, AHP, and the entropy weight method. Lastly, [PERSON] et al. [6; 7; 8] proposed an integrated model for container trains, focusing on maximizing revenue through stop schedule planning and space pre-allocation, and applied a linear transformation for solution effectiveness. These studies collectively enhance the understanding of efficient railway transportation organization, directly informing optimization strategies for China-Europe freight train transportation. Research into multi-modal railway transport organization is also crucial for optimizing the China-Europe train system. [PERSON] [9] developed a model for a container transport system that optimizes the operation of direct, aggregation, and transfer trains, aiming to reduce transport costs and container transit times for the China-Europe train. [PERSON] [10] proposed a three-level railway container system, offering an analytical evaluation of various container transport organization modes. [PERSON] [11] explored freight organization and container transfers within a railway container transport network, focusing on improving efficiency and the integration of railway and water transport. Lastly, [PERSON] et al. [12; 13; 14] introduced a mixed integer programming model to optimize transshipments at seaport rail terminals. This model aimed to minimize operational costs while considering a range of factors, demonstrating its application in effectively managing multi-modal transport logistics. These studies collectively provide valuable insights into the complexities of multi-modal transport, directly informing strategies to enhance the efficiency and effectiveness of the China-Europe freight train system. Moreover, the development of robust scheduling models for railway container transportation organizations, considering external factors, is a critical area of study. [PERSON] et al. [15; 16] introduced a rapid railway container transport organization model utilizing robust optimization techniques. Their model adopts a multi-objective approach, integrating considerations for the environment, traffic, municipal administration, and the economy. It establishes a near-optimal scheduling framework, effectively leveraging innovative solutions like the Metro Cargo terminal and Cargo Sprinter modular train. This model is designed to enhance the scheduling flexibility and resilience of railway container transport against external uncertainties, potentially offering significant improvements in operational efficiency and adaptability for such systems. In the realm of transportation organization optimization under uncertain parameters, fuzzy and stochastic programming emerge as two predominant methods. [PERSON] [17] applied fuzzy optimization to tackle uncertainties in transportation demand, transit time, and costs, formulating a fuzzy chance-constrained programming model that underscores the interplay between transportation cost, demand, and capacity. [PERSON] [18] addressed time uncertainties in container-rail routes using fuzzy variables, developing a dual-objective model optimized with fuzzy chance constraint programming for cost and emissions considerations. [PERSON] [19] proposed a multimodal transport model accounting for uncertain freight volume and arrival times, employing fuzzy programming and the NSGA-II algorithm for a solution. [PERSON] et al. [20; 21] crafted a multi-objective transportation problem that leverages fuzzy numbers to manage uncertainty in cost and time and solved it using LINGO-WINDOWS-64x86-18.0. [PERSON] et al. [22] utilized fuzzy theory and group decision making for assessing safety levels in road transport of hazardous goods. In contrast, [PERSON] et al. [23; 24; 25] employed stochastic programming and genetic algorithms to optimize reliable paths for emergency material transportation under dual uncertainties. [PERSON] et al.