چكيده به لاتين
The effects of disturbances in rail networks include delays and increased travel time. In the case of major disturbances, the delays cause the loss of reliability and dissatisfaction of the passengers. Re-scheduling in the rail system involves the use of effective strategies for managing operations and reducing delays during and after the disruption period in the shortest possible time. In this thesis, the occurrences of major disruptions in both the full or partial blockage of the route as well as the randomness of the running time in various scenarios are considered as the sources of the disruption. The time, duration and location of blockage are given. A simulation-based optimization model that includes dynamic dispatch priority rules is proposed in order to reduce the average total delay time of trains at stations. In addition, a multi-objective VND algorithm is proposed in order to (1) minimizing the total average delay of trains at the destination and (2) minimizing the total deviation from the initial schedule at certain points in the rail network. The variable neighborhood search algorithm is designed to manage traffic under bottleneck constraints and capacity close to the blockage. The new timetable resulting from this involves new dispatch times and train sequences. The performance of the proposed model is assessed using a variety of disruptive scenarios. The results show that the developed approach of this research, in terms of improving the convergence rate, increasing reliability, reducing average delay and improving the statistical indexes, has significant advantages in generating a reasonable solution to disruptive management solutions, compared to commercial software (OptQuest) and state-of-the-art solution method (NSGA II). In single-objective case, the proposed method could decrease the delay by approximately 12.48% and 29.18% compared to OptQuest and FCFS methods, respectively. By comparing the output of the proposed method with the optimal solution, it was found that the average value of the optimality gap is about 6%.
