چكيده به لاتين
Urban trains are a key tool in urban management for reducing air pollution and controlling traffic. With the increasing daily use of these systems, efforts are focused on maximizing the efficiency of the rail network. In such situations, any disruption in train scheduling can lead to network-wide disarray and, ultimately, passenger dissatisfaction. Given that real-world events are often uncertain, accounting for uncertainty in train scheduling becomes essential. Effective management of urban train scheduling under uncertain conditions is one of the core challenges in public transportation systems. These uncertainties can stem from factors such as sudden increases in demand, weather changes, accidents, or technical issues, all of which impact system performance and passenger satisfaction. In this study, mathematical models and optimization algorithms have been developed and evaluated to manage train rescheduling in response to uncertain conditions. First, the factors affecting train scheduling were identified and analyzed. Then, a deterministic model was designed to handle delays, and finally, to incorporate uncertainty, a scenario-based model was developed. The proposed model’s performance was evaluated using hypothetical data for a metro line. The results indicate that the proposed models can significantly improve the accuracy and efficiency of train scheduling under uncertain conditions, leading to reduced delays and increased passenger satisfaction.
