Abstract: China has operated the largest high-speed railway network in the world. However, the existing methods of operation management are not adjusted to fit the technology advantage and the new operation environment, leading to the restriction of the service improvement. The application of optimization technology and revenue management method to the rail passenger operation practice is necessary for improving the operation and service efficiency. A optimization model for large-scale rail passenger transportation operation is proposed in this paper, which combines line planning model and revenue management model. The new model can solve more complicated operation problem of the railway network with multi-lines, multi-trains, multi-discount levels and dynamic demand. It optimizes seat allocation among trains and finds the optimal train departure schedule to maximize the total operational revenue. The passengers' purchase behaviors is also considered in the model with estimated transfer probabilities between different ticket discount level. A column generation algorithm and two fast heuristic algorithms are introduced in this paper, which solve the large-scale mixed integer program model more efficiently. Using randomly generated data, a group of test models with two by two line network structure are solved by XPRESS software. Numerical results shows that the column generation algorithm and fast heuristic algorithms can reduce the model scales and computational complexity. The heuristic algorithms may increase the solving efficiency more than ten to hundred times with tiny sacrifice of solution accuracy. It's concluded that the new model and algorithm is suitable to solve large scale railway network optimization model which is close to real application.

Key words: optimization model, revenue management, passenger railway, column generation algorithm