Railway networks are subjected to disruptions on a daily basis, which may make the timetable unimplementable, which in its turn may significantly influence passenger satisfaction. In practice, train dispatchers are responsible for mitigating the influence of disruptions, such as delays of trains, train cancellations, and overcrowdedness at stations. The solutions they propose are highly dependent on their experience, often resulting in low quality solutions. In addition, the disruptions must be solved in a matter of minutes, which is challenging because of the problem scale and computational complexity. Effective models and solution approaches are required to mitigate the influence of disruptions. In recent decades, railway rescheduling models have been developed to support train dispatchers and to improve rail services. A recent and promising model is the event-activity network model, which is a graph-based formulation that supports a wide variety of rescheduling measures. This thesis extends the event-activity network model by including rolling stock circulation with depot entry and exit operations to increase the practicability of the operator-centric model. In addition, a passenger-centric model is proposed by embedding detailed passenger-related factors into the operator-centric model, where the train capacity is included, and the detailed number of passengers in the railway network is calculated. Therefore, the effect of delays on passengers can be handled properly. The passenger-centric model can help minimize the number of waiting passengers on platforms to avoid overcrowding and to improve passenger satisfaction. In practice, the resulting passenger-centric mixed-integer linear programming (MILP) problem is hard to solve due to the introduction of binary variables for train orders, which are important for calculating the detailed number of passengers. An adaptive large neighborhood search (ALNS) algorithm is introduced to address the complexity due to train orders and to improve the computational efficiency of the passenger-centric method. With properly designed destroy and repair operators, the ALNS algorithm can explore the solution space efficiently. Therefore, a balanced trade-off between solution time and quality can be made. Case studies are conducted based on the train lines operating between the stations of Utrecht and 's-Hertogenbosch in the Netherlands. The simulation results show that the developed model can explicitly include the number of passengers while considering the rolling stock circulation plan. Compared to directly solving an MILP problem using a commercial solver, ALNS can calculate solutions more efficiently while maintaining a high level of solution quality.

The railway timetable rescheduling problem is a challenging problem in both industry and academia. It is required to calculate a feasible and relatively good timetable within a limited time to reduce the negative impact of disturbances or disruptions. The railway timetable rescheduling problem is typically formulated as a mixed integer linear programming (MILP) problem, which is difficult to solve due to the existence of the integer variables. To address this problem, many optimization-based studies have been conducted. The main advantage of using optimization-based methods is that they are easy to implement and more straightforward. However, the main disadvantage is that most optimization-based methods cannot reach the time requirements for large railway timetable rescheduling problems. There are also some researches using reinforcement learning techniques to solve this problem. By using reinforcement learning, the time requirement could be fulfilled. In this thesis, an algorithm that combines both reinforcement learning and optimization approaches is proposed to solve the railway timetable rescheduling problem. In the beginning, the reinforcement learning environment is constructed from the railway timetable rescheduling problem. By selecting the independent integer variables as the action, the constraints involving the integer variables are satisfied. After that, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the complete solution of the integer variables could be derived from these independent integer variables. With the solution of integer variables, the MILP problem could be transformed into a linear programming problem, which could be solved efficiently. Several case studies are conducted in this thesis based on part of the Dutch railway network from Utrecht to 's-Hertogenbosch. The simulation results show that the proposed method makes a great improvement compared with the baseline regarding reducing the total delay of the system. Meanwhile, the reinforcement learning-based method also has an obvious advantage in terms of running time.