Railway Sciences (Jun 2024)
Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process
Abstract
Purpose – The 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/approach – To 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. Findings – Through 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/value – The 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.
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