# Robust Optimization of Transport Organization for China Railway Express
###### Abstract  
This paper presents an in-depth analysis of the robust optimization of the China-Europe freight train transportation organization under uncertain cargo transportation demand. The study commences by constructing a robust optimization model tailored for specific environments, which is further extended to address the complexities of uncertain freight demand. A notable aspect of this research is the adoption of an innovative approach to manage the uncertainties in freight transportation demand at each node, employing a box-type uncertainty set distribution. This methodology allows for an effective and balanced optimization strategy that accommodates the dynamic nature of demand fluctuations. The research findings underscore that increased robustness in the optimization model is associated with higher transportation costs within the China-Europe freight train network, especially under conditions of variable demand. The model demonstrates a preference for adjusting transportation costs to maintain the stability of the transportation scheme, particularly in response to wider variations in cargo demand. This strategy, prioritizing cost-effectiveness and adaptability, highlights the importance of a comprehensive approach to managing demand uncertainties. The significant contributions of this paper include the development of a robust, economically viable, and efficient transportation organization plan for China-Europe freight trains, equipped to navigate the challenges posed by uncertain cargo demand at the originating nodes. The study's emphasis on the practical application of advanced optimization techniques and uncertainty management methods marks a notable advancement in the field of freight train transportation. Additionally, the paper suggests avenues for further research in the intricate and evolving landscape of freight transportation, providing valuable insights for future studies. C 2023 2  
comprehensive approach to managing the uncertainties surrounding freight transportation demand at each node, while other parameters are considered deterministic. In this context, the transportation demand for goods at each node is modeled within a range of variability, allowing for a responsive and adaptable optimization strategy. This approach recognizes the fluctuating nature of demand, adapting the transportation organization to accommodate varying levels of cargo flow. This paper focuses on the robust optimization of the transportation organization for China-Europe freight trains. The aim is to address the complexities of cargo transportation demand and enhance the economic viability, efficiency, and stability of the China-Europe freight transportation system. This research holds substantial practical significance and offers valuable insights into the optimization of large-scale transportation networks. By exploring advanced optimization techniques and strategies for managing transportation uncertainties, this study contributes to the ongoing development and improvement of the China-Europe freight train operations. Optimizing railway container transportation organization is pivotal for enhancing China-Europe freight train operations. Several studies have contributed insights in this domain. [PERSON] et al. [1] developed a two-tier programming model aimed at maximizing revenue and minimizing CO\({}_{2}\) emissions and transportation costs in railways, employing a non-dominated sorting genetic algorithm with an elite strategy. [PERSON] [2] focused on railway coal transport, formulating a multi-objective optimization model to balance carrier income and transportation costs, solved using genetic algorithms. [PERSON] et al. [3] presented a high-speed railway freight service network model that balances costs and demand, resolved through a column generation algorithm, thereby optimizing operational costs. [PERSON] et al. [4] addressed the minimization of necessary train containers in marshalling stations, considering both carrier and shipper benefits.