基于人工蜂群算法的轨道交通列车行车间隔优化

轨道交通运营组织作为轨道交通运营企业管理的核心，在降低企业运营成本、提升服务水平和旅客出行效率方面起着非常重要的作用。提出一种基于人工蜂群（ABC）优化算法的列车行车间隔优化策略，在考虑运营企业和旅客各自利益的基础上，以列车发车间隔为决策变量，建立旅客平均候车时间最小和列车等候时间最大的双目标非线性规划模型。采用ABC算法对模型进行优化求解，结合京津城际铁路某日不同时段客流基础数据进行仿真，实例验证了所提算法和模型的有效性。

Train interval optimization of rail transit based on artificial bee colony algorithm

As the core of the operation and management of a rail transit enterprise, the rail transit operation organization plays a very important role in reducing the operation cost of the enterprise, improving the service level and the travel efficiency of passengers. A strategy based on Artificial Bee Colony (ABC) optimization algorithm was proposed to optimize the train traffic interval. Based on the consideration of the respective interests of operators and passengers, the train departure interval was taken as the decision variable to establish a bi-objective nonlinear programming model for the lowest average passenger waiting time and the largest train waiting time. Artificial Bee Colony (ABC) algorithm was used to optimize the model. The simulation results on Beijing-Tianjin inter-city passenger flow at different times of a day demonstrate the effectiveness of the proposed algorithms and models.