Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Yajing Zheng, Dekun Zhang
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Abstract

PurposeThe purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times. These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals. The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodology/approachTo achieve this objective, the paper simulates actual train operations, incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station. The Monte Carlo simulation method is adopted to solve this problem. This approach transforms a nonlinear model, which includes constraints from probability distribution functions and is difficult to solve directly, into a linear programming model that is easier to handle. The method then linearly weights two objectives to optimize the solution.FindingsThrough the application of Monte Carlo simulation, the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model. By continuously adjusting the weighting coefficients of the linear objectives, the method is able to optimize the Pareto solution. Notably, this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/valueThe paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times. The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement. Furthermore, the method’s ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.
基于随机原点和过程控制约束的高速铁路站场轨道利用优化研究
本文旨在消除区间运行时间偏斜分布造成的列车到达和出发时间波动。这些波动源于区间运行过程中的随机起源和过程因素,并可能在多个区间内累积。为实现这一目标,本文模拟了实际列车运行情况,将区间运行时间的波动纳入到车站到达和出发轨道的利用中。本文采用蒙特卡罗模拟法来解决这一问题。这种方法将包含概率分布函数约束且难以直接求解的非线性模型转化为易于处理的线性规划模型。研究结果通过蒙特卡罗模拟的应用,该研究成功地将带有概率分布函数约束的复杂非线性模型转化为易于管理的线性规划模型。通过不断调整线性目标的加权系数,该方法能够优化帕累托方案。值得注意的是,这种方法不需要大量的场景数据就能获得令人满意的帕累托解集。 原创性/价值 本文介绍了一种在区间运行时间波动的情况下优化高铁站到达和出发轨道利用率的新方法,为该领域做出了贡献。使用蒙特卡洛模拟将问题转化为可操作的线性规划模型是一项重大进步。此外,该方法无需依赖大量数据集即可生成令人满意的帕累托解决方案,这也增加了其在现实世界中的实用价值和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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