چكيده به لاتين
Urban metro scheduling has always had many complexities and problems, and with minimal change in scheduling, the entire system movement may be disturbed. Metro companies are always trying to implement a scheduling system that can best serve the customers of this system and use the resources that are used to build the metro. In order to achieve this goal, companies must always be aware of the real world issues and problems in their schedules, which can be achieved by uncertain modeling. Therefore, in this research, a robust optimization has been used to address real-world issues. This paper presents a robust model for scheduling train movements that simultaneously reduces passenger waiting times and optimizes electrical energy consumption for its purposes. The movement between the two stations is divided into three sections: acceleration, steady motion and braking, the duration of the train running in each of these sectors in three scenarios: high demand, normal demand and low demand. In the next step, gated algorithms and particle masses have been used to find the optimal answer in the case of definitive and scenario models. Using both algorithm methods, the scenario-based model provides better results than the definitive model. In order to compare the results of the four indicators, the total duration of the movement, the time of the train movement, the number of passengers waiting and the amount of energy needed for acceleration were used. The results of the scenario-driven model were 3.1, 11, 8.22 and 19.6 percent, respectively, compared to the current situation.
