Railway traffic is susceptible to unforeseen disruptions and delays that require rapid operational timetable adjustments. In this master's thesis, we address the problem of autonomous railway timetable rescheduling with the aim of minimizing secondary delays in real time. A mixed-integer linear programming model has been developed, based on an alternative graph and consistently taking into account safety and infrastructure constraints. Due to the computational complexity of the problem, a rolling horizon time decomposition method has been implemented, enabling a rapid system response. The core of the solution is a multi-objective optimization approach that combines the lexicographic method with Pareto frontier search. By incorporating real passenger count data, the system actively seeks trade-offs between the robustness of timetable rescheduling and passenger delays. The proposed model has been implemented in the Python programming language using the SCIP solver and evaluated on real-world examples from Slovenian Railways for the single-track Ljubljana -- Kamnik Graben railway line in 2025. The results of stress tests and a comparison with the UIC 406 capacity analysis method show that the developed system effectively resolves conflicts, limits the propagation of delays, and provides dispatchers with flexible strategies for railway traffic optimization.
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