Abstract
This study aims to enhance the accuracy of railway section capacity estimation in freight logistics by accounting for random delays inherent in semi-automatic blocking (SAB) systems. These systems are still widely used in regions such as Kazakhstan, where manual dispatching introduces variability that affects scheduling and throughput reliability. A discrete-event simulation model was developed using AnyLogic software to assess the impact of dispatch delays on capacity. The delays were modeled as random variables following three statistical distributions: normal (for stable operation), exponential (for high uncertainty), and empirical (based on observed field data). The model calculates train intervals and daily throughput under each delay scenario. The results show that ignoring stochastic delays may lead to capacity overestimation by up to 30%. Depending on the delay profile, estimated throughput ranged from 64 to 77 trains per day. The empirical distribution yielded the most realistic result, approximately 69 trains per day, closely reflecting real operational conditions. Accounting for delay variability improves the realism and accuracy of capacity estimation, especially in manually operated systems where automation is limited. The proposed method offers a decision-support tool for logistics planners and infrastructure managers to improve scheduling, evaluate operational risks, and guide investments in dispatch system modernization.