| Summary: | Urban subway scheduling is a quite complicated task as any small fluctuation could disturb the entire system. Subway companies need an efficient scheduling system that best serves passengers and optimizes the utilization of resources. They need, therefore, to include real-world problems in their schedule through non-deterministic modeling. A robust optimization approach is applied in real-world data to propose an optimum train schedule that minimizes passenger waiting time and maximizes electrical energy converted from kinetic energy. Three high, normal, and low travel demand scenarios are considered to optimize train running-time duration in all three interstation movement categories, namely acceleration, coasting, and braking. To find the optimal solution, a genetic algorithm (GA) model has been applied to real data from a subway line in Tehran Metro.
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