Application of particle swarm optimization to the train scheduling for high-speed passenger railroad planning

This paper presents an approach for solving the train scheduling for high-speed passenger railroad planning problem through the particle swarm optimization (PSO) for the first time. PSO has demonstrated the ability to deal with non-convex, non-linear, integer-mixed optimization problems. In this formulation, the objective function consists of two terms: the variation of inter-departure times for high-speed trains and the total travel time. This combination of terms results in a non-linear objective function. A case on the train scheduling for high-speed passenger railroad planning problem is presented to show the methodology's feasibility and efficiency, compared with the existing optimal planning methods, the search time of the particle swarm optimization method is shorter and the result is close to the ideal solution, simultaneously.