In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin–destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results.

在本文中，我们提出了一种基于模拟的城市快速公交网络的车头优化。基本的离散事件模拟模型包含多个随机元素，包括与时间有关的需求和转弯操作时间以及与方向有关的车辆行驶和乘客转移时间。旅客创造过程是一个泊松过程，它使用基于匿名移动电话和红外计数数据的每小时始发地-目的地矩阵。平台上和车辆内的乘客数量受到容量限制。作为微观元素，考虑了沿平台和车辆内的乘客分布。通过归一化和标量化，将涉及成本降低和服务水平提高的双目标问题转化为单目标优化问题。应用基于种群的进化算法和不同的解决方案编码变体。从基于实际数据（例如，维也纳地铁网络）的测试实例中获得计算经验。协方差矩阵适应进化策略在大多数情况下效果最佳，而新开发的编码可通过产生更好的短期结果来帮助加快优化过程。